Machine Learning Techniques for Assisted Reproductive Technology: A Review
Abstract
Infertility is becoming a public health issue in almost all countries. Assisted Reproductive Technology (ART) is considered as a method of last resort for treating infertility. The treatment of ART is highly expensive and painful, and also the probability of success is low since the success is affected by a large number of variables. Researchers are now trying to identify patterns comprising significant variables, their impact on success, and the interdependence of different variables to enumerate the status of the patient and to support the doctors and biologists to prescribe treatment to improve the probability of success of ART. Machine learning technique is a tool that is used by various researchers in the field of ART to identify the interlink between the variables. The objective of this review paper is to find the appliance of machine learning techniques in ART and to find further enrichment needed for future research. From the literature, it is found that some research works were done using machine learning techniques to predict ART outcome. On analyzing the reviews qualitatively and quantitatively, it is understood that various classifiers are used for ART outcome prediction but they are trained using limited amount of static data collected from fertility centers. The exact prediction of ART outcome may be improved by training the classifier with large amount of dynamic data. But building such a classifier is difficult by the already existing techniques. This may be made possible by introducing Big Data Analytics in ART.
This paper was recommended by Regional Editor Piero Malcovati.
