By Ayo Onikoyi

Kemi Akanbi, a Nigerian doctoral student at the University of Cincinnati, Ohio made a crucial scientific breakthrough in relation to a condition affecting women within peak childbearing years culminating in infertility.

In this study, Kemi Akanbi examines the significance of early detection and the role of AI in awareness, mitigation, and an overall impact on women’s health. PCOS, an acronym for Polycystic Ovary Syndrome in the words of the researcher is a condition triggered by hormonal imbalance in women that renders fertility futile.

In the same vein, it sparks a domino effect on patients’ health-giving rise to health challenges like HBP, Endometrial Cancer (cancer of the uterus), heart disease and depression. PCOS, a rife health condition with vague source(s) affects women irrespective of clime, creed or race. Her study revealed that a sizeable number of women are undiagnosed and less than half of the diagnosed women receive proper diagnosis.

In line with a personal and lifelong commitment towards intellectual contribution to academia and advancing women’s cause, Akanbi championed the ground breaking research to ameliorate detection, accurate prediction in an attempt to prevent the ensuing health challenges arising from PCOS by incorporating Artificial intelligence through a predictive machine learning model tasked to identify patients at risk of the condition and promptly alert health professionals culminating in early intervention.

Having rigorously experimented with a number of Predictive Machine Learning Models, followed by a thoroughly observed performance for accuracy, precision, and other metrics, results showed that a predictive model labeled Random Forest classifier outperformed other learning algorithms with a success rate of 96% on all evaluation metrics.

The exceptional performance demonstrated by her Random Forest Model in analyzing medical data, through swift and accurate detection of Polycystic Ovary Syndrome (PCOS) makes it suitable for real-time clinical applications, allowing for early diagnosis and personalized treatment plans. PCOS affects millions of women in the US, contributing significantly to infertility as reported by the CDC. Early detection through this model can significantly improve women’s reproductive health outcomes.

Her work has been presented at conferences such as the University of Cincinnati research symposium and has been well received by the scientific community.