AI-Driven Early Detection of Polycystic Ovary Syndrome via Follicle Count
- 1University of Tulsa
Abstract
Polycystic Ovary Syndrome is a common endocrine disorder characterized by ovulatory dysfunction, hyperandrogenism, and/or polycystic ovarian morphology, with significant reproductive and metabolic consequences. Due to heterogeneous symptom profiles, Polycystic Ovary Syndrome is frequently underdiagnosed or diagnosed late. In this study, we develop machine learning models for early Polycystic Ovary Syndrome prediction using a structured clinical dataset with 42 features and 542 patient records. After data cleaning and normalization, correlation-based feature selection was applied to retain the most predictive variables. Multiple models were trained and evaluated, including Logistic Regression, Decision Tree, KNN, and Random Forest. Results demonstrate that Random Forest achieves the best overall performance (approximately 88% accuracy), suggesting that ensemble models can effectively capture non-linear feature interactions in clinical data. We also contextualize findings with international clinical guidance and recent work on explainable and clinically applicable Polycystic Ovary Syndrome prediction systems.
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BibTeX
@misc{thota2026aidriven,
title = {{AI-Driven Early Detection of Polycystic Ovary Syndrome via Follicle Count}},
author = {D. Thota and A. Mahesha and M. F. Khasim and K. P. Kethineni and B. Pothireddygari and B. Rahmani},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.01.26361974},
url = {https://www.medrxiv.org/content/10.64898/2026.09.01.26361974v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - AI-Driven Early Detection of Polycystic Ovary Syndrome via Follicle Count
AU - D. Thota
AU - A. Mahesha
AU - M. F. Khasim
AU - K. P. Kethineni
AU - B. Pothireddygari
AU - B. Rahmani
PY - 2026
DA - 2026/09/01
PB - medRxiv
DO - 10.64898/2026.09.01.26361974
UR - https://www.medrxiv.org/content/10.64898/2026.09.01.26361974v1
AB - Polycystic Ovary Syndrome is a common endocrine disorder characterized by ovulatory dysfunction, hyperandrogenism, and/or polycystic ovarian morphology, with significant reproductive and metabolic consequences. Due to heterogeneous symptom profiles, Polycystic Ovary Syndrome is frequently underdiagnosed or diagnosed late. In this study, we develop machine learning models for early Polycystic Ovary Syndrome prediction using a structured clinical dataset with 42 features and 542 patient records. After data cleaning and normalization, correlation-based feature selection was applied to retain the most predictive variables. Multiple models were trained and evaluated, including Logistic Regression, Decision Tree, KNN, and Random Forest. Results demonstrate that Random Forest achieves the best overall performance (approximately 88% accuracy), suggesting that ensemble models can effectively capture non-linear feature interactions in clinical data. We also contextualize findings with international clinical guidance and recent work on explainable and clinically applicable Polycystic Ovary Syndrome prediction systems.
ER -