A Data-Driven Analysis Approach for Potential Infertility Treatments

Abstract

Infertility in modern society poses pressing issues to couples usually rooting back to the family. Infertility in women is mainly caused by PCOS (polycystic ovary syndrome) and abnormal sperm production in men is due to undescended testicles, genetic defects, and infections such as chlamydia, gonorrhea, or HIV. Infertile couples usually demand the success of the treatment process, and they have the right to do so, it is cost-effective. Treatment methods available today are generally very expensive and cost is a major factor for these couples. We have used machine learning concepts in this paper to determine potential infertility treatments focusing mainly on PCOS.