# Using machine learning to predict patients with polycystic ovary disease in Chinese women

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/using-machine-learning-to-predict-patients-with-polycystic-ovary-disease-in/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Chen-Yu Wang,Dee Pei,Chun-Kai Wang,Jyun‐Cheng Ke,Siou‐Ting Lee,Ta-Wei Chu,Yao-Jen Liang |
| Citations | 7 |
| DOI | 10.1016/j.tjog.2024.09.019 |
| Fields | Medicine |
| Open Access | true |
| OA Status | diamond |
| OA URL | https://doi.org/10.1016/j.tjog.2024.09.019 |
| OpenAlex ID | https://openalex.org/W4406171072 |
| PMID | 39794054 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Ta-Wei Chu](https://scholariq.org/researchers/ta-wei-chu/)

## Paper primary topic

- [Ovarian function and disorders](https://scholariq.org/topics/ovarian-function-and-disorders/)

## Paper topics

- [Ovarian function and disorders](https://scholariq.org/topics/ovarian-function-and-disorders/)
- [Systemic Lupus Erythematosus Research](https://scholariq.org/topics/systemic-lupus-erythematosus-research/)
- [Heart Rate Variability and Autonomic Control](https://scholariq.org/topics/heart-rate-variability-and-autonomic-control/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.