Authors : Chidimma Nweke; Prema Kirubakaran; Ridwan Kolapo

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/bdcu9jrc

Scribd : https://tinyurl.com/5t4vz9da

DOI : https://doi.org/10.38124/ijisrt/26aug048

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Polycystic Ovary Syndrome (PCOS) is a lead major disorder and primary cause of infertility in women of reproductive age, affecting about 13% of this population globally with over 70% of cases remaining undiagnosed. Early diagnosis is yet challenging, particularly in low-resource settings where ultrasound imaging is inaccessible. This study focuses on leveraging Random Forest (RF) model for PCOS prediction using only clinical and biochemical data, enhanced with Shapley Additive Explanations (SHAP) for model interpretability. A publicly available Kaggle PCOS dataset from 541 women (177 PCOS-positive, 364 negative) across 10 hospitals in Kerala, India, was utilised. A leakage-free preprocessing pipeline applied median and mode imputation before data splitting.

Keywords : Polycystic Ovary Syndrome, Random Forest, Shapley Additive Explanations, Machine Learning, Explainable Artificial Intelligence, Clinical Diagnosis.

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