A Comprehensive and Systematic Literature Review of Computational Intelligence Algorithms to Diagnose and Predict Female Infertility

Abstract

Recent researches inferred that one out of ten couple are facing fertility problem and it is observed that most of the cases address female infertility as a common health disorder due to the effect of several genetical, environmental and psychological factors. Efficient medical practices like IVF (In-vitro fertilization) are widely available in treating infertility problems in couples. As IVF procedure is influenced by several clinical attributes, predicting the rate of success is considered as prime concern. Apart from that, diagnosing ovulation disorders caused due to hormonal imbalances is considered as a challenging aspect. Technology assisted medication plays a vital role in addressing such problems. This article presents the comprehensive review of existing research studies that include a vast range of computational intelligence and machine learning algorithms addressing the problem of infertility in women. In addition, this article comprehensively illustrates a systematic analysis of existing research studies to identify the research gaps and challenging issues in devising computational intelligence algorithms for diagnosing and treating the infertility in women.