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

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

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Google Scholar : https://tinyurl.com/bdh5v7mu

Scribd : https://tinyurl.com/2fehemks

DOI : https://doi.org/10.38124/ijisrt/26may2179

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Abstract : Women with Polycystic Ovary Syndrome (PCOS) have a 2- to 4-fold increased risk of cardiovascular disease (CVD), yet early risk markers are poorly characterized. This study applies machine learning (ML) to identify the most predictive early markers of subclinical CVD in PCOS patients. A cross-sectional dataset of 3,872 PCOS women (ages 18-45 years) without known CVD underwent comprehensive clinical, biochemical, and vascular imaging assessments. Forty-seven candidate markers were evaluated. Three ML feature selection methods (LASSO regression, Random Forest importance, and Boruta algorithm) were applied, and markers consistently selected by at least two methods were validated using logistic regression with 5-fold cross-validation.

Keywords : Cardiovascular Risk, Early Markers, Machine Learning, PCOS, Feature Selection, Subclinical Atherosclerosis.

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