Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records

IntroductionPredictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict...

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Bibliographic Details
Main Authors: Zahra Zad, Victoria S. Jiang, Amber T. Wolf, Taiyao Wang, J. Jojo Cheng, Ioannis Ch. Paschalidis, Shruthi Mahalingaiah
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Endocrinology
Subjects:
polycystic ovary syndrome (PCOS)
disease prediction
predictive model
machine learning
artificial intelligence
Online Access:https://www.frontiersin.org/articles/10.3389/fendo.2024.1298628/full

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https://www.frontiersin.org/articles/10.3389/fendo.2024.1298628/full

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