The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> The authors propose a theoretical framework for backpropagation (BP) in order to identify some of its limitations as a general learning procedure and the reasons for its success in several experiments on pattern recognition. The first important conclusion is that examples can be found in which BP gets stuck in local minima. A simple example in which BP can get stuck during gradient descent without having learned the entire training set is presented. This example guarantees the existence of a solution with null cost. Some conditions on the network architecture and the learning environment that ensure the convergence of the BP algorithm are proposed. It is proven in particular that the convergence holds if the classes are linearly separable. In this case, the experience gained in several experiments shows that multilayered neural networks (MLNs) exceed perceptrons in generalization to new examples. > <s> BIB001 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. > <s> BIB002 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Measurements of extracellular water (ECW) and total body water (TBW) can be useful when assessing the nutritional status of surgical patients. The simple and non-invasive nature of bio-impedance techniques may allow repeat measurements to assess clinically significant changes in ECW and TBW. We have evaluated bio-impedance spectroscopy (BIS) and multi-frequency bio-impedance analysis (MFBIA) using radioisotope dilution as the independent method. <s> BIB003 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> The Self-Organizing Map (SOM) is a vector quantization method which places the prototype vectors on a regular low-dimensional grid in an ordered fashion. This makes the SOM a powerful visualization tool. The SOM Toolbox is an implementation of the SOM and its visualization in the Matlab 5 computing environment. In this article, the SOM Toolbox and its usage are shortly presented. Also its performance in terms of computational load is evaluated and compared to a corresponding Cprogram. <s> BIB004 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> There are a number of different quantitative models that can be used in a medical diagnostic decision support system (MDSS) including parametric methods (linear discriminant analysis or logistic regression), non-parametric models (K nearest neighbor, or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. Practitioners are left to either choose a favorite model or to test a small subset using cross validation methods. This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS. The topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level. These targets can then be used in model selection, variable reduction, parameter determination, and to assess the adequacy of the clinical measurement system. These ideas are applied to a successful model selection for a real-world breast cancer database. Diagnostic accuracy results are reported for individual models, for ensembles of neural networks, and for stacked predictors. <s> BIB005 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. In this paper, we have explored new techniques of patient assessment that may prospectively identify this minority of patients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over 4 month's time. Patients monitored their pain and activity levels continuously over the first 3 weeks. Pain and functional status were assessed at baseline and at 3 weeks following enrollment. Follow-up assessment of functional status and progress were performed at 2 and 4 months. The pain and activity levels were categorized using a self-organizing-map neural network. A back-propagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r e 0.96, p < 0.01) and mental health (r e0.80, p < 0.01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain. <s> BIB006 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Classification of dengue using the current World Health Organization (WHO) system is not straightforward. In a large prospective study of pediatric dengue, no clinical or basic laboratory parameters clearly differentiated between children with and without dengue, although petechiae and hepatomegaly were independently associated with the diagnosis. Among the 712 dengue-infected children there was considerable overlap in the major clinical features. Mucosal bleeding was observed with equal frequency in those with dengue fever and dengue hemorrhagic fever (DHF), and petechiae, thrombocytopenia, and the tourniquet test differentiated poorly between the two diagnostic categories. Fifty-seven (18%) of 310 with shock did not fulfill all four criteria considered necessary for a diagnosis of DHF by the WHO, but use of the WHO provisional classification scheme resulted in considerable over-inflation of the DHF figures. If two separate entities truly exist rather than a continuous spectrum of disease, it is essential that some measure of capillary leak is included in any classification system, with less emphasis on bleeding and a specific platelet count. <s> BIB007 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> In this paper, centrifuge-based microfluidic platforms are reviewed and compared with other popular microfluidic propulsion methods. The underlying physical principles of centrifugal pumping in microfluidic systems are presented and the various centrifuge fluidic functions such as valving, decanting, calibration, mixing, metering, heating, sample splitting, and separation are introduced. Those fluidic functions have been combined with analytical measurement techniques such as optical imaging, absorbance and fluorescence spectroscopy and mass spectrometry to make the centrifugal platform a powerful solution for medical and clinical diagnostics and high-throughput screening (HTS) in drug discovery. Applications of a compact disk (CD)-based centrifuge platform analyzed in this paper include two-point calibration of an optode-based ion sensor, an automated immunoassay platform, multiple parallel screening assays, and cellular-based assays. The use of modified commercial CD drives for high-resolution optical imaging is discussed as well. From a broader perspective, we compare technical barriers involved in applying microfluidics for sensing and diagnostics as opposed to applying such techniques to HTS. The latter poses fewer challenges and explains why HTS products based on a CD fluidic platform are already commercially available, while we might have to wait longer to see commercial CD-based diagnostics. <s> BIB008 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Gastrointestinal (GI) diseases, the third most common cause of deaths in the UK (59,685 in 2000) have increased by 25% in the last ten years. Gastro-oesophageal reflux disease (GORD) has a prevalence of 20-40% in the population. Ten to 15% of GORD patients have Barrett's oesophagus, a change in the cell lining of the oesophagus linked to oesophageal cancer. With a 5 year survival of 9% and annually 7000 new cases (12 per 100,000) oesophageal cancer has increased by 50% in the last 20 years. Barrett's oesophagus is associated with poor motility, reflux and hiatus hernia. Oesophageal manometry is used to investigate oesophageal motility. Patterns of pressure changes are seen as peristaltic waves pass over transducers on a catheter within the oesophagus. The standard investigation has many problems, as symptoms rarely occur during the short investigation time period. Also, despite widespread use it is not clear what values; and patterns are important. Ambulatory manometry, introduced in 1985 to address these issues, has proved unpopular due to the large amounts of data generated which has proved difficult to analyse. Manual analysis is very time consuming while current software analysis, using predefined rules to detect peaks and classify patterns, is not always trusted and has not advanced over the last 20 years. Our approach has been to use Kohonen self organising feature maps to classify the patterns of data that occur over 24 hours. Following a simple process of identifying candidate periods (feature vectors) this technique was used to automatically find patterns or clusters within the raw data. The early results have shown that patterns of oesophageal manometry data can be identified. Different patterns can be analysed and variation in occurrence rates detected during specific symptomatic periods. The results suggest that this approach may enable detection and help quantify differences in 24 hour manometry data between healthy controls and patients with Barrett's oesophagus. <s> BIB009 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Dengue fever (DF) is an acute febrile viral disease frequently presented with headache, bone or joint and muscular pains, and rash. A significant percentage of DF patients develop a more severe form of disease, known as dengue haemorrhagic fever (DHF). DHF is the complication of DF. The main pathophysiology of DHF is the development of plasma leakage from the capillary, resulting in haemoconcentration, ascites, and pleural effusion that may lead to shock following defervescence of fever. Therefore, accurate prediction of the day of defervescence of fever is critical for clinician to decide on patient management strategy. To date, no known literature describes of any attempt to predict the day of defervescence of fever in DF patients. This paper describes a non-invasive prediction system for predicting the day of defervescence of fever in dengue patients using artificial neural network. The developed system bases its prediction solely on the clinical symptoms and signs and uses the multilayer feed-forward neural networks (MFNN). The results show that the proposed system is able to predict the day of defervescence in dengue patients with 90% prediction accuracy. <s> BIB010 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> This paper introduces a novel approach to classify the risk in dengue hemorrhagic fever (DHF) patients using the bioelectrical impedance analysis (BIA) technique. This in vivo technique involves the application of a small average constant current of less than 1 mA at a single frequency of 50 kHz through the human body, and measurement of the body's bioelectrical resistance (R), phase angle (/spl alpha/), body capacitance (BC) and capacitive reactance (X/sub c/) via four surface electrodes. BIA measurements have been conducted on 184 (97 males and 87 females) serological confirmed dengue patients during their hospitalization in University Kebangsaan Malaysia Hospital, Malaysia. The patients included in the study were DHF I-IV according to World Health Organization criteria. Univariate analysis of variance is used for assessing the relationship between gender and group with the bioelectrical tissue conductivity (BETC) parameters. Experimental findings show that BETC, as reflected by reactance, is the key determinant indicator for classifying risk category in the DHF patients. Hence, this novel approach of the BIA technique can provide a rapid, noninvasive, and promising method for classifying and evaluating the risk of the DHF patients. <s> BIB011 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that leads to irreversible joint destruction. To prevent this, new biological therapies, such as infliximab, have been successfully developed. The present analysis is based on an expanded access program in which 511 RA patients with chronic refractory disease were treated with infliximab. They received a standard dose of 3mg/kg on weeks 0, 2, 6, 14 and every 8 weeks thereafter. On week 22, the treating rheumatologist evaluated the situation of every patient and decided whether the current dose should be increased or not. This decision can be considered as a measure of insufficient response. In the present analysis, 3 machine-learning classification techniques-the self-organizing map (SOM), multilayered perceptron (MLP) and support vector machine (SVM)-are implemented to model the decision to give a dose increase. Their performance on increasingly multivariate real-life data will be studied and compared to classical statistics-linear discriminant analysis (LDA) and logistic regression (LR). Results show that the SOM is an excellent tool for data visualization but not for classification. All the remaining methods show good classification performance, if configured well. However, as the number of features increases, the performance decreases. The SVM suffers to a lesser degree from this curse of dimensionality. Expectation maximization (EM) comes out as a good method to cope with missing values in such real-life data. <s> BIB012 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> This communication describes a simple method for patterning paper to create well-defined, millimeter-sized channels, comprising hydrophilic paper bounded by hydrophobic polymer. We believe that this type of patterned paper will become the basis for low-cost, portable, and technically simple multiplexed bioassays. We demonstrate this capability by the simultaneous detection of glucose and protein in 5 μL of urine. The assay system is small, disposable, easy to use (and carry), and requires no external equipment, reagents, or power sources. We believe this kind of system is attractive for uses in less-industrialized countries, in the field, or as an inexpensive alternative to more advanced technologies already used in clinical settings.[1-4] ::: ::: The analysis of biological fluids is necessary for monitoring the health of populations,[2] but these measurements are difficult to implement in remote regions such as those found in less-industrialized countries, in emergency situations, or in home health-care settings.[3] Conventional laboratory instruments provide quantitative measurements of biological samples, but they are unsuitable for these situations since they are large, expensive, and require trained personnel and considerable volumes of biological samples.[2] Other bioassay platforms provide alternatives to more expensive instruments,[5-7] but the need remains for a platform that uses small volumes of sample and that is sufficiently inexpensive to be used widely for measuring samples from large populations. ::: ::: We believe that paper may serve as a particularly convenient platform for running bioassays in the remote situations locations. As a prototype for a mthod we believe to be particularly promosing, we patterned photoresist onto chromatography paper to form defined areas of hydrophilic paper separated by hydrophobic lines or “walls”; these patterns provide spatial control of biological fluids and enable fluid transport, without pumping, due to capillary action in the millimeter-sized channels produced. This method for patterning paper makes it possible to run multiple diagnostic assays on one strip of paper, while still using only small volumes of a single sample. In a fully developed technology, patterned photoresist would be replaced by an appropriate printing technology, but patterning paper with photoresist is: i) convenient for prototyping these devices, and ii) a useful new micropatterning technology in its own right. ::: ::: We patterned chromatography paper with SU-8 2010 photoresist as shown in Figure 1a and as described below: we soaked a 7.5-cm diameter piece of chromatography paper in 2 mL of SU-8 2010 for 30 s, spun it at 2000 rpm for 30 s, and then baked it at 95 °C for 5 min to remove the cyclopentanone in the SU-8 formula. We then exposed the photoresist and paper to 405 nm UV light (50 mW/cm2) for 10 s through a photo-mask (CAD/Art Services, Inc.) that was aligned using a mask aligner (OL-2 Mask Aligner, AB-M, Inc). After exposure, we baked the paper a second time at 95 °C for 5 min to cross-link the exposed portions of the resist. The unpolymerized photoresist was removed by soaking the paper in propylene glycol monomethyl ether acetate (PGMEA) (5 min), and by washing the pattern with propan-2-ol (3 × 10 mL). The paper was more hydrophobic after it was patterned, presumably due to residual resist bound to the paper, so we exposed the entire surface to an oxygen plasma for 10 s at 600 millitorr (SPI Plasma-Prep II, Structure Probe, Inc) to increase the hydrophilicity of the paper (Figures 2a and 2b). ::: ::: ::: ::: Figure 1 ::: ::: Chromatography paper patterned with photoresist. The darker lines are cured photoresist; the lighter areas are unexposed paper. (a) Patterned paper after absorbing 5 μL of Waterman red ink by capillary action. The central channel absorbs the sample ... ::: ::: ::: ::: ::: ::: Figure 2 ::: ::: Assays contaminated with (a) dirt, (b) plant pollen, and (c) graphite powder. The pictures were taken before and after running an artificial urine solution that contained 550 mM glucose and 75 μM BSA. The particulates do not move up the channels ... ::: ::: ::: ::: The patterned paper can be derivatized for biological assays by adding appropriate reagents to the test areas (Figures 1b and ​and2b).2b). In this communication, we demonstrate the method by detecting glucose and protein,[8] but the surface should be suitable for measuring many other analytes as well.[7] The glucose assay is based on the enzymatic oxidation of iodide to iodine,[9] where a color change from clear to brown is associated with the presence of glucose.[10] The protein assay is based on the color change of tetrabromophenol blue (TBPB) when it ionizes and binds to proteins;[11] a positive result in this case is indicated by a color change from yellow to blue. ::: ::: For the glucose assay, we spotted 0.3 μL of a 0.6 M solution of potassium iodide, followed by 0.3 μL of a 1:5 horseradish peroxidase/glucose oxidase solution (15 units of protein per mL of solution). For the protein assay, we spotted 0.3 μL of a 250-mM citrate buffer (pH 1.8) in a well separate from the glucose assay, and then layered 0.3 μL of a 3.3 mM solution of tetrabromophenol blue (TBPB) in 95% ethanol over the citrate buffer. The spotted reagents were allowed to air dry at room temperature. This pre-loaded paper gave consistent results for the protein assay regardless of storage temperature and time (when stored for 15 d both at 0 °C and at 23 °C, wrapped in aluminum foil). The glucose assay was sensitive to storage conditions, and showed decreased signal for assays run 24 h after spotting the reagents (when stored at 23 °C); when stored at 0 °C, however, the glucose assay was as sensitive after day 15 as it was on day 1. ::: ::: We measured artificial samples of glucose and protein in clinically relevant ranges (2.5-50 mM for glucose and 0.38-7.5 μM for bovine serum albumin (BSA))[12, 13] by dipping the bottom of each test strip in 5 μL of a pre-made test solution (Figure 2d). The fluid filled the entire pattern within ca. one minute, but the assays required 10-11 min for the paper to dry and for the color to fully develop.[14] In all cases, we observed color changes corresponding roughly in intensity to the amount of glucose and protein in the test samples, where the lowest concentrations define the lower limits to which these assays can be used (Figure 2e). For comparison, commercially-available dipsticks detect glucose at concentrations as low as 5 mM[7, 9] and protein as low as 0.75 μM;[6, 15] these limits indicate that these paper-based assays are comparable in sensitivity to commercial dipstick assays. Our assay format also allows for the measurement of multiple analytes. ::: ::: This paper-based assay is suitable for measuring multiple samples in parallel and in a relatively short period of time. For example, in one trial, one researcher was able to run 20 different samples (all with 550 mM glucose and 75 μM BSA) within 7.5 min (followed by another 10.5 min for the color to fully develop). An 18-min assay of this type—one capable of measuring two analytes in 20 different sample—may be efficient enough to use in high-throughput screens of larger sample pools. ::: ::: In the field, samples will not be measured under sterile conditions, and dust and dirt may contaminate the assays. The combination of paper and capillary action provides a mechanism for separating particulates from a biological fluid. As a demonstration, we purposely contaminated the artificial urine samples with quantities of dirt, plant pollen, and graphite powder at levels higher than we might expect to see in the samples in the field. These particulates do not move up the channels under the action of capillary wicking, and do not interfere with the assay (Figure 3). ::: ::: Paper strips have been used in biomedical assays for decades because they offer an inexpensive platform for colorimetric chemical testing.[1] Patterned paper has characteristics that lead to miniaturized assays that run by capillary action (e.g., without external pumping), with small volumes of fluids. These methods suggest a path for the development of simple, inexpensive, and portable diagnostic assays that may be useful in remote settings, and in particular, in less-industrialized countries where simple assays are becoming increasingly important for detecting disease and monitoring health,[16, 17], for environmental monitoring, in veterinary and agricultural practice and for other applications. <s> BIB013 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> This study presents a new approach to determine the significant prognosis factors in dengue patients utilizing the self-organizing map (SOM). SOM was used to visualize and determine the significant factors that can differentiate between the dengue patients and the healthy subjects. Bioimpedance analysis (BIA) parameters and symptoms/signs obtained from the 210 dengue patients during their hospitalization were used in this study. Database comprised of 329 sample (210 dengue patients and 119 healthy subjects) were used in the study. Accordingly, two maps were constructed. A total of 35 predictors (17 BIA parameters, 18 symptoms/signs) were investigated on the day of defervescence of fever. The first map was constructed based on BIA parameters while the second map utilized the symptoms and signs. The visualized results indicated that, the significant BIA prognosis factors for differentiating the dengue patients from the healthy subjects are reactance, intracellular water, ratio of the extracellular water and intracellular water, and ratio of the extracellular mass and body cell mass. <s> BIB014 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Even though the World Health Organization criteria's for classifying the dengue infection have been used for long time, recent studies declare that several difficulties have been faced by the clinicians to apply these criteria. Accordingly, many studies have proposed modified criteria to identify the risk in dengue patients based on statistical analysis techniques. None of these studies utilized the powerfulness of the self-organized map (SOM) in visualizing, understanding, and exploring the complexity in multivariable data. Therefore, this study utilized the clustering of the SOM technique to identify the risk criteria in 195 dengue patients. The new risk criteria were defined as: platelet count less than or equal 40,000 cells per mm(3), hematocrit concentration great than or equal 25% and aspartate aminotransferase (AST) rose by fivefold the normal upper limit for AST/alanine aminotransfansferase (ALT) rose by fivefold the normal upper limit for ALT. The clusters analysis indicated that any dengue patient fulfills any two of the risk criteria is consider as high risk dengue patient. <s> BIB015 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Detection of non-structural protein 1 (NS1) of dengue virus can gives early diagnosis of dengue and shows high sensitivity. Current technique uses for the dengue NS1 detection is enzyme-linked-immusorbent assay (ELISA) on 96 microwell plate. However, the assay requires long incubation time about 90 minutes (for antigen-antibody interaction) and total assay time takes almost 2 hours and 30 minutes to complete. Therefore, a lab-on-a-disk with applied centrifugal force is proposed as a potential microfluidic platform to reduce the assay time by effectively mix and separate liquid in the ELISA assay. The advantages of the technique are having large specific volume, short diffusion length, minimum reagents consumption and simplify procedures. The lab-on-a-disk will exploit centrifugal and capillary forces to act as a passive valve to control the flow sequence of different solutions involved. Each steps of the ELISA process is carried out automatically by controlling the rotation speed of the disk. This paper will describe the lab-on-a-disk platform on its microfluidic principles, fabrication process, detection systems, biosensor applications, and the proposed model for dengue NS1-ELISA assay. <s> BIB016 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Abstract This study reports an integrated microfluidic system which utilizes virus-bound magnetic bead complexes for rapid serological analysis of antibodies associated with an infection by the dengue virus. This new microfluidic system integrates one-way micropumps, a four-membrane-type micromixer, two-way micropumps and an on-chip microcoil array in order to simultaneously perform the rapid detection of immunoglobulin G (IgG) and immunoglobulin M (IgM). An IgM/IgG titer in serum is used to confirm the presence of dengue virus infection. By utilizing microfluidic technologies and virus-bound magnetic beads, IgG and IgM in the serum samples are captured. This is followed by purification and isolation of these beads utilizing a magnetic field generated from the on-chip array of microcoils. Any interfering substances in the biological fluids are washed away automatically by the flow generated by the integrated pneumatic pumps. The fluorescence-labelled secondary antibodies are bound to the surface of the IgG/IgM complex attached onto the magnetic beads. Finally, the entire magnetic complex sandwich is transported automatically into a sample detection chamber. The optical signals are then measured and analyzed by a real-time optical detection module. The entire process is performed automatically on a single chip within 30 min, which is only 1/8th of the time required for a traditional method. More importantly, the detection limit has been improved to 21 pg, which is about 38 times better when compared to traditional methods. This integrated system may provide a powerful platform for the rapid diagnosis of dengue virus infection and other types of infectious diseases. <s> BIB017 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%. <s> BIB018 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> The scope of the difficulties that has been addressed in dengue disease includes the definition of the risk criteria in dengue disease and the prediction of the risk in dengue patients. It is critical to precisely and efficiently predict the level of risk in dengue disease for clinical care, surveillance and lifesaving. Even though some studies showed significant results in this area, a complete, systematic approach for predicting the risk in dengue disease has never been attained yet. Therefore, this study was carried out to develop a noninvasive intelligent technique for predicting the risk in dengue patients. A combination of the self-organizing map (SOM) and multilayer feed-forward neural networks (MFNN) was employed for this task. Clinical manifestations and bioelectrical impedance analysis (BIA) parameters belonging to the dengue patients were considered for this aim. The SOM was used to define the significant risk predictors, whereas the MFNN was employed for constructing the prediction model. Seven significant risk predictors as defined by SOM were employed for the dengue patient risk classification. The MFNN prediction model defined by 10 hidden neurons, momentum of 0.99, learning rate of 0.1 and iteration rate of 20,000 achieved a 70% predicative accuracy with 0.121 sum squared error. <s> BIB019 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> The enzyme-linked immunosorbent assay (ELISA) is widely used in medical diagnostics. In order to reduce the diagnosis time and to lower the consumption of sample/reagents in an ELISA assay, a suction-type, automatic, pneumatically-driven microfluidic chip has been designed and fabricated in this study. The microfluidic chip integrates a multi-functional micro-transport/mixing unit, for transporting metering and mixing of samples and reagents in order to automatically perform the entire ELISA protocol. A new surface modification has been adopted which allows for a high processing capacity. The detection sensitivity for the dengue virus is found to be 101 PFU/ml, which is much better than a conventional ELISA assay (103 PFU/ml). The entire assay time is only 30 min, which is much faster than with 96-well microtiter plates (4 h). The consumed sample and reagent volume is only 12 μl, which is less than a conventional assay (100 μl). The development of this microfluidic chip may be promising for other immunosensing applications. <s> BIB020 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> Dengue disease is considered as one of the life threatening disease that has no vaccine to reduce its case fatality. In clinical practice the case fatality of dengue disease can be reduced to 1% if the dengue patients are hospitalized and prompt intravenous fluid therapy is administrated. Yet, it has been a great challenge to the physicians to decide whether to hospitalize the dengue patients or not due to the overlapping of the medical diagnosis criteria of the disease. Beside that physicians cannot decide to admit all patients because this will have major impact on health care cost saving due to the huge incident of dengue disease in the country. Even if the physicians managed to identify the critical cases to be hospitalized, most of the tools that have been used for monitoring those patients are invasive. Therefore, this study was conducted to develop a non-invasive accurate diagnostic system that can assist the physicians to diagnose the risk in dengue patients and therefore attain the correct decision. Bioelectrical Impedance Analysis measurements, Symptoms and Signs presented with dengue patients were incorporated with Adaptive Neuro-Fuzzy Inference System (ANFIS) to construct two diagnostic models. The first model was developed by systematically optimizing the initial ANFIS model parameters while the second model was developed by employing the subtractive clustering algorithm to optimize the initial ANFIS model parameters. The results showed that the ANFIS model based on subtractive clustering technique has superior performance compared with the other model. Overall diagnostic accuracy of the proposed system is 86.13% with 87.5% sensitivity and 86.7% specificity. <s> BIB021 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Self-Organizing Map (SOM) <s> This paper presents a theoretical development and critical analysis of the burst frequency equations for capillary valves on a microfluidic compact disc (CD) platform. This analysis includes background on passive capillary valves and the governing models/equations that have been developed to date. The implicit assumptions and limitations of these models are discussed. The fluid meniscus dynamics before bursting is broken up into a multi-stage model and a more accurate version of the burst frequency equation for the capillary valves is proposed. The modified equations are used to evaluate the effects of various CD design parameters such as the hydraulic diameter, the height to width aspect ratio, and the opening wedge angle of the channel on the burst pressure. <s> BIB022
The self-organizing map (SOM) is an unsupervised neural network that is considered as one of the most powerful aids for visualizing, analyzing, and understanding the complexity of high-dimensional data. It receives a number of different multivariable input samples, discovers significant relationships in these samples, and presents them into a two dimensional map. This map contains different data clusters (prototypes), each of them consisting of samples that have similar features. Similarly, relationships within the data and cluster structures can be visualized and interpreted. Therefore, the SOM can be considered as an exploratory data analysis tool for generating hypotheses on the relationships among the data. The SOM has been widely used in medical applications BIB005 BIB009  BIB006 BIB012 . Typical SOM can be visualized by using the U-matrix and the component planes. The U-matrix visualizes the distances between map units which is used to show the cluster structure of the map. Normally it is colored and these colors represent the distance between map units BIB004 . Colors with high values in the U-matrix indicate a cluster border while colors with low value indicate the clusters themselves. The value of the color is presented in the color scale beside the U-matrix. On the other hand, the component planes present values of all variable in each U-matrix map unit. In dengue infections, the SOM was employed to identify the non-invasive significant prognosis factors that can distinguish between dengue patients and healthy subjects and also distinguish between the male and female patients BIB014 . The study presented a new approach to determine the significant prognosis factors in dengue patients utilizing the SOM. This technique showed the significant factors that can differentiate between dengue patients and the healthy subjects. Three hundred twenty-nine samples (210 dengue patients and 119 healthy subjects) were used in the study. Each sample contained 35 predictors (17 BIA parameters, 18 symptoms/signs). Two SOMs were constructed as shown in Figure 2 . Each map contains the U-matrix (on the top left of the map) and the component planes that represent the value of the variable in the U-matrix. The bottom left of each map shows the labels which indicate the type of patient in each cluster of the U-matrix map (healthy subject (H) or dengue patients (D)).The first map was constructed based on the BIA parameters data (variables) while the second map utilized the symptoms and signs data (variables). By visualizing the U-matrix and investigating the similarity between the clusters in the U-matrix and the component planes, the correlations between the dengue patients and the prognosis factors form the symptoms and signs and BIA parameters can be defined. The results revealed that, the significant BIA prognosis factors for differentiating the dengue patients from the healthy subjects were reactance, ICW, ratio of ECW/ICW, and ratio of the extracellular mass to body cell mass. On the other hand, abdominal epigastic pain, petechiea rash, and bleeding tendency were the main signs and symptoms that were present in dengue patients. Due to the limitations of the WHO 1997 classification criteria that have been used to classify the severity of dengue patients, Faisal et al. BIB015 employed clustering, the SOM's technique to determine new criteria that may help classify dengue patients based on disease severity. This technique aims to apply the K-mean clustering technique to cluster the SOMʼs prototypes rather than clustering the data directly to enhance the data clustering. Generally, the implementation of this approach is performed in two stages: First, the SOM is trained to identify the prototypes of the dengue patients' data, and second, the K-mean clustering technique is implemented to cluster those prototypes. As a result, three criteria were then defined to classify the level of risk in dengue patients. The results were validated by comparing them to some other dengue researchers' findings as well as the WHO criteria   . By using this technique, important results were obtained: the new risk criteria classified 33% of the DF patients as high risk dengue patients. Those patients might not be hospitalized according to the WHO criteria since they were classified as DF patients. However, those patients were classified as high risk by using the new criteria and thereby they might be at risk and face death if they are not closely monitored to detect the onset of plasma leakage. Another significant result was that 65.5% and 57.7% of the patients who were classified by WHO as DHF I and DHF II, respectively, were classified by the new criteria as low risk dengue patients. Those patients need not be hospitalized since they are classified as low risk patients and therefore the savings on the cost of the hospital admissions can be substantial. This result agrees with other researchers' findings BIB007 and the recent WHO guidelines which indicates there is a problem using the existing WHO classification due to the changes in the epidemiology of dengue, and there is a high potential for the clinicians' decision to be based the levels of severity for classifying the patients  . ANNs have been successfully applied to several problems in dengue disease. Ibrahim et al. BIB010 employed the multi-layer perceptron (MLP) network trained via the back-propagation (BP) algorithm to develop a prediction system for predicting the day of fever reduction in dengue patients due to the fact that the progression of the DHF patient to DSS occurs following this day. Ninety percent prediction accuracy was achieved by using this approach. The study concluded that since most of the dengue patients were sick during or around the time of fever reduction, this ANN might be very promising to assist clinicians in the early determination of prognosis and in prescribing the management plan for their patients. The network architecture designed by Ibrahim et al. BIB010 has been used to develop a GUI as shown in Figure 3 . The user interface is comprised of a few dialog boxes and radio buttons that request patient information and input data for the prediction process. The patient information includes patient identification and gender. The radio buttons are used to simplify user selection of the symptoms and the signs presented by the dengue patients. The "predict the day of defervescence of fever" button gives the estimated day of fever reduction. The entire system was compiled to function as a standalone application that can be used in any computer environment. Ibrahim et al. BIB011 introduced a novel approach to classify the risk in DHF patients by using the BIA technique. A total of 184 (97 males and 87 females) serologically confirmed dengue patients (DHF I-IV) were studied during their hospitalization. The relationship between gender and group with the biological electrical tissue conductivity BETC parameters was studied employing univariate analysis of variance. The experimental results showed that BETC, specifically the reactance, was a potentially useful tool in classifying the risk factor of DHF patients. The work by Ibrahim et al. BIB011 has been extended to diagnose the risk in dengue infections using an artificial neural network technique BIB018 . The study employed MLFFNN for classifying the risk stages in dengue patients. In this study, the severity of risk in dengue disease was quantified using the dengue patients' blood data based on threshold values obtained from other researcher's findings and the WHO classification system  . Data comprises of 223 healthy subjects and 207 dengue patients were arranged randomly into the training and testing in the ratio of 70:30. The ANN was trained via the steepest descent back propagation with momentum algorithm method. The optimum network architecture was determined by optimizing the training parameters. The optimization criteria was the sum squared error (SSE) and total classification accuracy of the network. The total classification was subjected to a 25% error tolerance. After the optimal ANN structure was determined, it was pruned using a weight eliminating method to enhance the system performance. The overall classification accuracy was 96.27% with 95.88%, 96.83%, and 95.81% for high risk, low risk, and healthy groups, respectively. Continuing research has taken the neural network architecture obtained from Ibrahim et al. BIB018 . and developed an automatic dengue risk classification. Using the Matlab software, a GUI has been developed for use in real clinical applications as shown in Figure 4 . The user interface is comprised of a few dialog boxes and radio buttons that request patient information and input data for the diagnosis process. The DIAGNOSE button is used to initiate MLFFNN calculation for each given input. The system produces one of three outcomes: healthy, low risk, or high risk; and the result is displayed in the bottom most dialog box. The same technique and the same data were used by Faisal et al. BIB019 to develop a non-invasive intelligent technique for diagnosing the risk stages in dengue patients using clinical manifestations (signs and symptoms) and the BIA measurements. An accuracy of 70% with 0.121 sum squared error was achieved by this model. The study concluded that such a screening system can aid physicians in the diagnosis of the risk and the prognosis of dengue patients, but it will not be definitive. Although the Multi-Layer Perceptron (MLP) trained via the back propagation (BP) algorithm has demonstrated significant good performance in classification and prediction applications compared to statistical analysis, it suffers from a slow convergence rate and often yields suboptimal solutions  BIB001 . To overcome this drawback, many researchers have employed the Levenberg-Marquardt (LM) algorithm  or Scaled Conjugate Gradient (SCG) algorithm BIB003 for training the MLP since these methods provide faster convergence and better estimation results. Those algorithms have been successfully used in medical applications for classifying and diagnosing several diseases. Faisal et al. BIB019 constructed the dengue patient diagnostic model using the LM and SCG algorithms. Systematic procedures involving training, testing, and validation were followed to construct the diagnostic model so that a higher performance of the diagnostic model can be achieved and the robustness of overall diagnostic models can be maintained. Precise tuning of the internal training algorithms' parameters was performed to attain the optimal model. Three-layer network is used. The activation function in hidden layer's neurons is hyperbolic tangent sigmoid while in the output layer's neurons are sigmoid transfer function. The 5-fold Cross Validation (CV) technique is implemented. The data are divided into five sets; each set contains 101 samples (45 high risk patients, 56 low risk patients). Four sets (404 samples) were used for training and the remaining set was used for testing. The training process was repeated for five times, at each time one of the sets was used as testing set. The results for optimization of the MFNN trained via the Levenberg-Marquardt algorithm showed that the optimal model achieved an average diagnostic accuracy of 70.7% with 73% sensitivity, 74% specificity and a 0.02 average MSE. By implementing the scaled conjugate gradient algorithm, the optimal diagnostic model achieved an average diagnostic accuracy of 75% with 0.01957 average MSE. Even with the success of ANNs in a decision support system, the use of computerized decision making systems in clinical medicine is rather difficult due to the uncertainty of naturally occurring diseases. In such a situation, fuzzy set theory appears as an appropriate tool for a decision making system since it deals with uncertainty by applying our knowledge and experience directly without any explicit mathematical models. Fuzzy logic describes human thinking and reasoning in a mathematical framework by using several rule bases (IF-THEN) that require a number of human experts to carefully define the rules. Even though the fuzzy logic has been successfully implemented, there are some basic aspects of it that are in need of better understanding. First, the need for a standard method for transforming human knowledge or experience into the rule base and database of a fuzzy inference system is noted. Second, there is a need for effective methods for tuning the membership functions BIB002 . Based on those needs, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed to serve as a basis for constructing a set of fuzzy if-then rules with appropriate membership functions to generate the stipulated input-output pairs BIB002 . Faisal et al. BIB021 utilized (ANFIS) to develop a dengue patient diagnostic model. The development of the model was carried out in two steps: defining the initial ANFIS model architecture and training of the defined model. Two approaches were followed to define the initial ANFIS model architecture. In the first approach, the number of membership functions in the inputs and the output were systematically varied and the effect of this variation in the model performance was investigated. In the second approach, a subtractive clustering algorithm was assigned to determine the initial ANFIS model by optimizing the number of membership functions and fuzzy rules. After the initial model structure was defined, it was trained so that the differences between the output obtained from the model and actual output are minimized. The hybrid learning algorithm was employed for this task. The results of the first approach showed that the highest overall accuracy of 80.19% with 71% sensitivity and 86% specificity was achieved. For the second approach, average diagnostic accuracy was 86.13% with 87.5% and 86.7 sensitivity and specificity, respectively. The graphic user interface for the new ANFIS is shown in Figure 5 . The user interface is comprised of four parts: patient information such as patient's identification, symptoms and signs, the bioelectrical impedance data, and the dengue patient risk diagnostic which determines the level of risk in dengue patients. Microfluidic devices have been proposed as a solution to overcome the many problems caused by the conventional dengue testing systems such as the high cost, reagent consumption, long turnaround time and complex procedures. Lab-on-a-disc (LOD) and lab-on-a-chip (LOC) are the most popular microfluidic platforms that have been reported by many researchers as a portable diagnostic device for dengue detection BIB022  BIB008 . Ibrahim et al.  and Yusoff et al. BIB016 have discussed the fundamentals of the microfluidic compact disc (CD) and its application as a platform for ELISA detection of dengue non-structural glycoprotein 1 (NS-1), and Ibrahim et al.  designed and fabricated a microfluidic CD to automatically perform an ELISA test for dengue detection. On the other hand, lab-on-a-chip (LOC) has been proposed as a precise, rapid, and low cost platform for dengue detection BIB017 BIB020 . Lee et al. BIB017 reported an integrated microfluidic platform that can detect the dengue virus by coating magnetic microbeads with antibody. A multi-way micropump shown in Figure 6 is used for moving the serum sample, reagents and other buffers inside the microfluidic net (channels and chambers). Lee et al. [72] claimed that the time required to perform one test is 30 min, which is only 1/8th of the time required to detect dengue using a conventional testing platform. Wenget al. BIB020 reported a new microfluidic LOC platform for dengue detection where a suction method (instead of pumping) is used to move the liquid inside the device. Polydimethylsiloxane (PDMS) has been used for the chip fabrication and the surfaces are pre-functionalized to minimized protein adsorption. The dengue testing process can be finished within 30 min and the chip can stay stable for one month if it is stored at 4 °C. The lab-on-paper, or paper-based diagnostic platform is a recently developed low cost method of implementing point of care diagnosis. The paper-based devices are easy to use, low cost, disposable, and can be used for a wide range of biomedical diagnosis BIB013   . A medical patch developed by Martinez et al. BIB013 allows for the use of chromatography paper as a low cost, low volume, and portable bioassay. The paper is patterned with test areas which are doped with the required reagents, which change in color when it reacts with the intended analytes. Matthews et al.  further enhanced the paper-based platform by developing an object identification algorithm that is light enough for use on mobile phones. The application utilizes the built-in camera of mobile phones for image capturing of the medical patch developed by Martinez et al. BIB013 . Once the image is captured, a developed mobile application then performs the necessary image processing and determines the disease state. In a novel study conducted by Lo et al.  , dengue virus is detected on a paper-based device for the detection of dengue virus by first amplifying the nucleic acids via reverse transcription loop-mediated isothermal amplification (RT-LAMP), then analysing the results using a colorimetric assay on paper. The process of RT-LAMP is performed on Dengue serotype-2 ribonucleic acid (RNA) using conventional microwell assay technique. Once the RT-LAMP process is completed, the DNA product is then dropped on a waxed patterned 96-well paper where it is mixed with biotin-11-deoxyuridine, and then conjugated with streptavidin horseradish peroxidase. The 96-well paper is then washed and scanned to obtain the colorimetric results on a computer.The study concluded that the combination of RT-LAMP andpaper-based colorimetric approach reduces the process time, while requiring very little sample volume and is suitable for the point-of-care application.