A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> One of the critical issues in data mining process especially for organizations is evaluating the success level of performed data mining projects. The purpose of this research is designing a Fuzzy expert system for the evaluation of success level of data mining projects based on quality of CRISP-DM methodology phases as one of the famous data mining methodologies. Here the CRISP-DM phases are specified as inputs of Fuzzy Inference System (FIS) model and the output is the success level of data mining project. This system has been designed by MATLAB software and has been implemented for a data mining project in an Iranian Bank as empirical study. <s> BIB001 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> Accurate and early prediction of the outcome of an in vitro fertilization (IVF) treatment is important for both patients and physicians. The most common question asked by IVF patients is ''What are my chances of conceiving?'' The answer to this difficult question typically considers patient age, day 3 serum follicle stimulating hormone (FSH) levels, and infertility diagnosis. However, many more parameters are known to affect IVF outcome. It is difficult for the clinician to recognize trends and intuitively decide how to optimize success rates for each infertile couple. This paper presents a hybrid intelligence method which integrating genetic algorithm and decision learning techniques for knowledge mining of an IVF medical database. The proposed method can not only assist the IVF physician in predicting the IVF outcome, but also find useful knowledge that can help the IVF physician tailor the IVF treatment to the individual patient with the aim of improving the pregnancy success rate. The twenty-eight most significant attributes for determining the pregnancy rate (e.g., patient's age, number of embryo transferred, number of frozen embryos, and culture days of embryo) and their combinative relationships (represented by if-then rules) were identified through the proposed method. The knowledge discovered in this study is currently accepted as an interesting discovery from the viewpoint of domain experts. For the results from this study to be conveniently accessed by IVF physicians and patients, an expert system tool equipped with the proposed IVF outcome prediction model was built. <s> BIB002 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> Since the introduction of in vitro fertilization (IVF) in 1978, over five million babies have been born worldwide using IVF. Contrary to the perception of many, IVF does not guarantee success. Almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. The decision to start or pursue with IVF is challenging due to the high cost, the burden of the treatment, and the uncertain outcome. In optimal counseling on chances of a pregnancy with IVF, prediction models may play a role, since doctors are not able to correctly predict pregnancy chances. There are three phases of prediction model development: model derivation, model validation, and impact analysis. This review provides an overview on predictive factors in IVF, the available prediction models in IVF and provides key principles that can be used to critically appraise the literature on prediction models in IVF. We will address these points by the three phases of model development. <s> BIB003 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction. <s> BIB004 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment. <s> BIB005 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature. <s> BIB006 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 1: ART Data Set <s> Objective To develop a random forest model (RFM) to predict implantation potential of a transferred embryo and compare it with a multivariate logistic regression model (MvLRM), based on data from a large cohort including in vitro fertilization (IVF) patients treated with the use of single-embryo transfer (SET) of blastocyst-stage embryos. Design Retrospective study of a 2-year single-center cohort of women undergoing IVF or intracytoplasmatic sperm injection (ICSI). Setting Academic hospital. Patient(s) Data from 1,052 women who underwent fresh SET in IVF or ICSI cycles were included. Intervention(s) None. Main Outcome Measure(s) The performance of both RFM and MvLRM to predict pregnancy was quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), classification accuracy, specificity, and sensitivity. Result(s) ROC analysis resulted in an AUC of 0.74 ± 0.03 for the proposed RFM and 0.66 ± 0.05 for the MvLRM for the prediction of ongoing pregnancies of ≥11 weeks. This RFM approach and the MvLRM yielded, respectively, sensitivities of 0.84 ± 0.07 and 0.66 ± 0.08 and specificities of 0.48 ± 0.07 and 0.58 ± 0.08. Conclusion(s) The performance to predict ongoing implantation will significantly improve with the use of an RFM approach compared with MvLRM. <s> BIB007
In order to successful knowledge discovery in databases (KDD), well-defined and formal methods should be applied for managing data. Cross-industry standard process for data mining (CRISP-DM) model is a standard methodology, which includes six phases: 1) problem domain understanding, 2) data understanding, 3) data preparation, 4) modeling, 5) evaluation and 6) deployment (using the discovered knowledge) BIB001 BIB002 . The dataset construction is followed by data entry process of medical records and data pre-processing. Data preprocessing is one of the most important steps and critical in the success of machine learning techniques  . The purpose of this step is to construct the final dataset from original raw data set as optimum input into the modeling algorithms BIB001 . Data preparation is performed by data cleaning, handling of missing values, outlier data and applying of normalization methods. The missing values of numerical features are replaced with median and nominal attributes are filled by mode of their corresponding feature  . Preparing of data also completes with feature selection (FS) and ranking algorithms. At this point, after attribute extraction from clinical practice guidelines, papers and experts opinion, by applying FS algorithms the final feature set is building as potential predictors for ART outcome. FS algorithms recruit to identify features that have important predictive role. These techniques do not change the content of initial features set, only select a subset of them. The purpose of feature selection is help to create optimize and cost-benefit models for enhancing prediction performance. There are different FS techniques, such as, filter techniques (Information gain, Gain ratio, etc.) that use to select and rank the optimal number of attributes. These algorithms score each feature, and then based on superior performance of prediction models, low-weighted features are removed  . Reporting of potential predictors in the literature without using the same initial feature set and categories, leads to difficult result interpretation and comparison BIB003 . Therefore, in this study we categorize different variables into subgroups (i.e. patient-related clinical and demographics Information, female/male pathology, oocyte stimulation and morphology, semen analysis, embryological data and lab tests) that have contribution on prediction of ART outcome. Table 1 , shows characteristics of examined datasets on 20 studies. All of these studies, except one of them BIB004 , have performed feature selection techniques on their datasets. The age of woman attribute is the most important feature in many (i.e. 9 studies) of machine learning based prediction studies on ART outcome   BIB002      BIB006 . The maximum number of features was 64 BIB005 , and the minimum number of features was four  , which were recruited in ART predictive modeling studies. Of the all papers on ART prediction model, five papers used more comprehensive features set, according to Table 1 BIB002   BIB006 BIB007 .