AI-Driven Early Detection of Polycystic Ovary Syndrome via Follicle Count
· 2026-09-04 · 原文
DOI：10.64898/2026.09.01.26361974v1?rss=1
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 ov
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1. 人话版
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.
2. 领域脉络
来源板块：板块一 · 研究前沿。
3. 机制拆解
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.
4. 证据与数字
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.
5. 反例与边界
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