Exploring Machine Learning Techniques for Male Infertility Prediction: A Review

Infertility, also known as sterility in both men and women, is a global health problem that affects the quality of life of couples who want to have children. In recent decades, technological developments in medicine and computer science have inspired the exploration of machine learning techniques to support early prediction of male infertility. In this paper, the authors present a comprehensive review of the various machine learning techniques that have been applied to male infertility prediction. The authors begin by outlining the background of male infertility and the complexity of its clinical diagnosis. We then detail the advantages of machine learning techniques in processing and analyzing complex health data, as well as their potential to provide new insights into the causative factors of male infertility. Through in-depth analysis, we identify several machine learning approaches commonly used in the literature, such as regression and classification. We also review a series of recent studies that applied these techniques in diagnosing male infertility. In addition, the authors highlight the challenges faced by researchers in using machine learning for infertility prediction, including the lack of high-quality data and the interpretability of complex models. Nevertheless, many studies have shown positive results in using machine learning techniques to contribute to the development of decision support systems in this field. To support the quality of research, future research that can be done by conducting a comprehensive review of various techniques in deep learning in detecting infertility diseases, especially in men.