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Comparative Analysis of Machine Learning Algorithms for Predicting Cardiovascular Risk in PCOS


Authors : Agbetayo Oke Kehinde; Agbetayo Juwon Christianah; Adeoba Oluwafemi Elisha; Isijola Ibiso Bukola

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/2s4am7j5

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DOI : https://doi.org/10.38124/ijisrt/26may2180

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Abstract : Polycystic Ovary Syndrome (PCOS) affects 8-13% of reproductive-aged women and is associated with a 2- to 4-fold increased risk of cardiovascular disease (CVD). However, optimal risk prediction algorithms for this population remain unclear. This study compares the performance of six machine learning (ML) algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, LightGBM, and a Deep Neural Network (DNN) for predicting 5-year CVD risk in women with PCOS.

Keywords : Machine Learning, Cardiovascular Risk, PCOS, XGBoost, Predictive Modeling, Algorithm Comparison.

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Polycystic Ovary Syndrome (PCOS) affects 8-13% of reproductive-aged women and is associated with a 2- to 4-fold increased risk of cardiovascular disease (CVD). However, optimal risk prediction algorithms for this population remain unclear. This study compares the performance of six machine learning (ML) algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, LightGBM, and a Deep Neural Network (DNN) for predicting 5-year CVD risk in women with PCOS.

Keywords : Machine Learning, Cardiovascular Risk, PCOS, XGBoost, Predictive Modeling, Algorithm Comparison.

Paper Submission Last Date
31 - July - 2026

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