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Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 28 May 2024 / Approved: 28 May 2024 / Online: 28 May 2024 (12:46:59 CEST) Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S. Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60. Preprints 2024, 2024051865. https://doi.org/10.20944/preprints202405.1865.v1 Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S.
Abstract Polycystic ovary syndrome (PCOS) is a common condition affecting women worldwide. In this paper, we analyse physical and mental health factors affecting PCOS in women. We collected data in the form of questionnaires from women belonging to diverse backgrounds and performed a sensitivity analysis to find which factors are significant in determining the presence or absence of PCOS in women and further predict the severity of the symptoms.
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