Aug 2026· International Journal of Innovative Science and Research Technology· 0 citations· 21 references
TL;DR
This study focuses on leveraging Random Forest model for PCOS prediction using only clinical and biochemical data, enhanced with Shapley Additive Explanations (SHAP) for model interpretability.
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.
Polycystic Ovary Syndrome (PCOS) is a disease that has spread across the globe and has become a significant health concern that mainly affects women of reproductive age. The detection, diagnosis, treatment, and management of the condition at an early stage are vital in order to lower the risk of long-term complications...
Pooja Snehal Janwe, Nazia Nusrath Ul Ain, K. Radhika et al.· International Conference on...· 0 citations
Combining hybrid ensemble learning, two-stage feature selection, and XAI approaches provides a computationally efficient, dependable, and interpretable method for PCOS diagnosis and practitioners may find this model to be a useful decision-support tool that improves the accuracy of diagnosis and lessens the need for hu...
Md Rakibul Hasan Efty, M. Rohman, K. M. Uddin et al.· Health Science Reports· 0 citations
This study develops machine learning models for early Polycystic Ovary Syndrome prediction using a structured clinical dataset with 42 features and 542 patient records and demonstrates that ensemble models can effectively capture non-linear feature interactions in clinical data.
D. Thota, A. Mahesha, M. F. Khasim et al.· medRxiv· 0 citations
SHAP (SHapley Additive exPlanations) analysis supports the assertion that the quantity of follicles, FSH/LH ratio, and the level of LH should be considered the most prevalent predictors, and that the evidence provided by the analysis can be interpreted by clinicians and corresponds to the Rotterdam diagnostic criteria.
Arya Malode, Vijayshri A. Injamuri, Vikul J. Pawar et al.· International journal of com...· 0 citations
Polyendocrine metabolic ovarian syndrome (PMOS) previously known as Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, yet its diagnosis remains challenging because accurate assessment often depends on investigations that are costly, less accessible, and difficult to sc...
A. Adegoke, Idris Babalola, P. A. Odesola· Journal of Engineering Resea...· 0 citations
Findings indicate that explainable machine learning models, particularly KNN and XGBoost, provide accurate and interpretable decision support for early PCOS screening, enabling timely intervention and offering a promising foundation for intelligent healthcare decision-support systems.
Sana Rubab, Musarrat Shaheen, Zohrain Tabassum et al.· Biomedical Informatics and S...· 0 citations
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