Leveraging Hybrid Machine Learning Models for Early Diagnosis and Prediction of Polycystic Ovary Syndrome (PCOS)

Abstract

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, characterized by irregular menstrual cycles, polycystic ovaries and hyperandrogenism. Early diagnosis is essential for managing symptoms and alleviating the risk of long-term health complications, including infertility, diabetes, and cardiovascular diseases. Early detection and proper management of PCOS are very essential and reduces the chances of complications. However, the lack of a reliable biomarker and due to its diverse presentation, it is very challenging to diagnose PCOS.  Machine learning techniques are a boon in PCOS diagnosis in improving the effectiveness and accuracy. This paper explores various ML techniques—including supervised learning algorithms such as logistic regression, support vector machines, and random forests, as well as deep learning methods like convolutional neural networks—for detecting PCOS from clinical, hormonal, and imaging data.  It highlights the potential of ML to not only assist in early diagnosis but also to create customized treatment plans based on patient-specific data. This paper aims at enhancing the diagnostic process and reducing human error by carefully investigating the works carried out in PCOS.  It also addresses the associated challenges like data quality, clinical implementation etc., to improve the healthcare of women with PCOS.