Enhanced Prediction of PCOS and PCOD using Deep Learning for Early Diagnosis and Clinical Risk Stratification
DOI:
https://doi.org/10.15662/IJARCST.2026.0903001Keywords:
Deep Learning, Machine Learning, Convolutional Neural Networks, Vision Transformers (ViT), Explainable Artificial Intelligence (XAI), Django Web FrameworkAbstract
Polycystic Ovary Syndrome and Polycystic Ovarian Disease are common endocrine disorders affecting women of reproductive age, often causing infertility, hormonal imbalance, and metabolic issues. Early diagnosis is difficult due to overlapping symptoms and limitations of traditional methods. This study proposes a machine learning–based predictive framework that integrates conventional algorithms with deep learning models such as CNNs and Vision Transformers (ViTs) for accurate early detection and risk stratification. The system identifies hidden patterns and disease indicators that are often missed in manual diagnosis. It also incorporates explainable AI techniques to improve transparency and support clinical decision-making. The framework is deployed as a scalable web-based application using Django, enabling real-time prediction and seamless healthcare integration. Overall, the proposed model improves diagnostic accuracy, supports personalized treatment, and enhances women’s healthcare outcomes through efficient and non-invasive analysis.
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