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Jin Z, Wang Y, Zhang X, et al. Role of machine learning in the diagnosis and prediction of reproductive disorders: A comprehensive review. Front Endocrinol (Lausanne). 2021;12:697962. doi:10.3389/fendo.2021.697962
Nandi A, Chen Z, Patel R, et al. Machine learning-based identification of clinical phenotypes and biomarkers for PCOS diagnosis using non-invasive features. J Clin Endocrinol Metab. 2020;105(5):1728-1736. doi:10.1210/clinem/dgaa078
Roy KK, Kumar N, Saxena A, et al. Predictive model for PCOS using machine learning techniques with non-invasive data. J Obstet Gynaecol Res. 2021;47(2):760-766. doi:10.1111/jog.14591
Liang B, Liu X, Wang Y, et al. A machine learning model for polycystic ovary syndrome diagnosis based on phenotypic and genetic data. Endocr Connect. 2021;10(7):817-827. doi:10.1530/EC-21-0103
Zhang J, Li M, Chen Q, et al. Predictive models for polycystic ovary syndrome based on machine learning techniques. J Biomed Inform. 2019;93:103149. doi:10.1016/j.jbi.2019.103149
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