A survey of multiple classifier systems as hybrid systems <s> Coronary diseases <s> When either detection rate (sensitivity) or false alarm rate (specificity) is optimized in an artificial neural network trained to identify myocardial infarction, the increase in the accuracy of one is always done at the expense of the accuracy of the other. To overcome this loss, two networks that were separately trained on populations of patients with different likelihoods of myocardial infarction were used in concert. One network was trained on clinical pattern sets derived from patients who had a low likelihood of myocardial infarction, while the other was trained on pattern sets derived from patients with a high likelihood of myocardial infarction. Unknown patterns were analyzed by both networks. If the output generated by the network trained on the low risk patients was below an empirically set threshold, this output was chosen as the diagnostic output. If the output was above that threshold, the output of the network trained on the high risk patients was used as the diagnostic output. The dual network correctly identified 39 of the 40 patients who had sustained a myocardial infarction and 301 of 306 patients who did not have a myocardial infarction for a detection rate (sensitivity) and false alarm rate (1-specificity) of 97.50 and 1.63%, respectively. A parallel control experiment using a single network but identical training information correctly identified 39 of 40 patients who had sustained a myocardial infarction and 287 of 306 patients who had not sustained a myocardial infarction (p = 0.003). <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Coronary diseases <s> Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation. <s> BIB002 </s> A survey of multiple classifier systems as hybrid systems <s> Coronary diseases <s> In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis. <s> BIB003 </s> A survey of multiple classifier systems as hybrid systems <s> Coronary diseases <s> In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis. <s> BIB004
A recent instance of CDSS is the application to cardiovascular disease diagnosis of an heterogenous collection of classifiers, composed of SVM, bayesian networks and ANN BIB002 finding ten new biomarkers. In this AptaCDSS-E process starts with the use of an aptamer biochip scanning protein expression levels which is the input to physician taking the decisions afterwards. Feature selection is performed by an ANOVA analysis. Doctor decisions are stored for system retraining. Classifier combination is done by majority voting or hierarchical fusion. Many CAD systems related with coronary diseases are based on the information provided by the electrocardiogram (ECG), so that many of them rely on the features extracted from them. Coronary artery disease is a broad term that encompasses any condition that affects the heart. It is a chronic disease in which the coronary arteries gradually harden and narrow, there have approaches to provide CAD for this condition, such as the use of a mixture of three ANNs for the prediction of coronary artery disease BIB003 . The dysfunction or abnormality of one or more of the heart four valves is called valvular heart disease. Its diagnosis is performed by neural network ensembles in BIB003 BIB004 over features selected by a correlation analysis with the categorical variable. Two separate ANNs are trained to identify myocardial infarction on training sets with different statistics regarding the percentage of patients in BIB001 . The network specialized in healthy controls is applied to the new data, if the output is below a threshold the subject is deemed healthy, otherwise the disease-specific network is applied to decide.