A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 2: Predictive Modeling <s> BACKGROUND ::: Evidence-based morphological embryo scoring models for ranking of implantation potential are still scarce, and the need for a precise model increases when aiming for singleton pregnancies. ::: ::: ::: METHODS ::: Prospectively, 2266 IVF/ICSI double-embryo, day 2 transfers were studied. The five variables scored in 3- to 5-step scales for the embryos transferred are blastomere number (BL), fragmentation, blastomere size variation ('equality', EQ), symmetry of the cleavage and mononuclearity in the blastomeres (NU). The scoring results of embryos with an individual traceability from scoring to implantation, i.e. treatments resulting in either no implantation (n=1385) or twin implantation (n=228), were studied for prognostic potential. ::: ::: ::: RESULTS ::: Although all five variables correlated highly with implantation potential, only BL, NU and EQ remained independently significant after regression analysis. The equation thus derived formed the basis for a 10-point integrated morphology cleavage (IMC) embryo score. A table with the scoring point for each possible combination of the embryo variables is presented. The scoring model was statistically validated on the singleton pregnancy group (n=653). ::: ::: ::: CONCLUSIONS ::: We suggest that this IMC embryo scoring, incorporating cleavage stage and information on the variation in blastomere size and the number of mononucleated blastomeres, may optimize embryo ranking and selection for day 2 transfers. <s> BIB001 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 2: Predictive Modeling <s> The IVF ET method is a scientifically recognized infertility treatment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treatment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process. <s> BIB002 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 2: Predictive Modeling <s> Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and complex owing to the formation of organized registries and incorporation of biological data. Typically, conventional statistical methods are used for the development of outcome prediction models and risk scores. However, these analyses carry inherent properties limiting their ability to cope with large data sets with multiple variables and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM). It enables prediction in complex data scenarios, familiar to practitioners and researchers. Technological and commercial applications are all around us, gradually entering clinical research. In the following review, we would like to expose hematologists and stem cell transplanters to the concepts, clinical applications, strengths and limitations of such methods and discuss current research in HSCT. The aim of this review is to encourage utilization of the ML and DM techniques in the field of HSCT, including prediction of transplantation outcome and donor selection. <s> BIB003 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> Phase 2: Predictive Modeling <s> This chapter introduces the foundations of machine learning that are necessary to motivate its combination to evolutionary algorithms and image processing. In the chapter, we will describe several machine learning tools, suitable for a wide variety of data and tasks. The goal is not to describe the total number of techniques, but instead to present a general view of the field through the description of the mail problems considered in machine learning. <s> BIB004
To develop more accurate prediction models with high-performance capacity, advanced computational techniques and data mining methods could be employed  . The focus of machine learning and data mining (DM) techniques are on developing computerized and efficient predictive modeling by exploring hidden and unknown patterns in data to discover knowledge with high accuracy. However, conventional statistical methods are fitted to predefined models BIB002   . The purpose of machine learning is to design and develop prediction models, by allowing the computer learn from data or experience to solve a certain problem BIB004 . Today, there is a growing trend in using of machine learning on vast variety of subjects in health care (41) . A practical application of machine learning in medical industry is clinical decision support systems (CDSS), that help to precise designation of treatment plan BIB003 . There are two major types of machine learning approaches: (i) supervised and (ii) unsupervised learning. In supervised learning, the training data set are labeled. The goal of supervised learning algorithms is to predict the value of target variable. Therefore, supervised learning adjusted for predictive modeling. Most commonly machine learning algorithms which are called classification algorithms are related to this category. Unsupervised learning is used in descriptive modeling and unlabeled data, by detecting underlying patterns with unlabeled data. Clustering and association rules are typically unsupervised learning algorithms. The most familiar classification algorithms are support vector machines (SVM), neural network (NN), k-nearest neighborhood (kNN), naive bayes (NB), random forest (RF) and decision tree (DT) which are commonly used to develop a prediction models. In the rest of this paper, we will investigate these algorithms in studies related to ART outcome prediction. In spite of the efficiency of the machine learning approaches as computational techniques to predict ART treatment outcome, there are few number of studies in this field  , Studies with complete aspects of ART data and sufficient number of effective variables are rare  BIB001 . In this study, we provide an overview on prediction models in ART. Our search retrieved 20 studies which are applied supervised machine learning algorithms in IVF or ICSI settings. The characteristics of the prediction models in these papers are summarized in Table 2 . Statistical methods are not suitable in prediction of ART outcome (11) then in this paper the statistical approaches are excluded .