A Comprehensive Machine Learning-Based Predictive Platform for Early Detection and Analysis of Infertility in Both Male and Female Using Ensemble Learning Techniques

Abstract

The inability to conceive after 12 months of consistent, unprotected sexual activity is known as infertility, a common reproductive health problem. Millions of couples worldwide are impacted by it, and it can be caused by issues pertaining to the male, female, or both partners. Genetic disorders, hormonal imbalances, structural abnormalities, infections, and lifestyle factors like poor diet, smoking, alcohol consumption, and stress can all contribute to male infertility. A thorough medical history, physical examination, semen analysis, and additional diagnostic testing as needed are typical components of evaluation. Ovulatory conditions like polycystic ovary syndrome (PCOS), fallopian tube damage, uterine abnormalities, endometriosis, or age-related decline in fertility are frequently linked to female infertility. Hormonal evaluations, ultrasound imaging, and specialized procedures like laparoscopy in certain situations are among the diagnostic techniques used for women. Depending on the underlying cause, treatment options for infertility may involve medication, surgery, lifestyle changes, or assisted reproductive technologies like intracytoplasmic sperm injection (ICSI) and in vitro fertilization (IVF). Accurate diagnosis and successful treatment depend on a comprehensive assessment of both partners. Reproductive medical advancements keep improving treatment results, giving infertile couples more hope and a higher standard of living.