Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches

2026-07-18

AI Summary

This study addresses the limitations of traditional polycystic ovary syndrome (PCOS) diagnosis, which relies on costly and time-consuming clinical and laboratory assessments. To enable efficient early detection, the authors propose a machine learning–based approach that integrates Random Forest feature importance with Highest Correlation (HC) analysis for robust feature selection. The performance of several ensemble models—including CatBoost, XGBoost, LightGBM, AdaBoost, and Random Forest—is systematically evaluated. Experimental results demonstrate that an AdaBoost classifier trained on ten optimally selected features achieves the highest accuracy on the test set, significantly enhancing both the automation and predictive precision of PCOS diagnosis. These findings underscore the strong potential of data-driven methodologies in improving diagnostic workflows for PCOS.

Abstract

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate; they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature Engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy.

Problem

• Polycystic Ovary Syndrome
• PCOS diagnosis
• machine learning
• early detection
• medical data

Innovation

• Feature Selection
• Machine Learning
• AdaBoost
• Polycystic Ovary Syndrome
• Data-driven Diagnosis