AI-driven Detection of PCOS for Personalized Health Improvement

Polycystic Ovary Syndrome (PCOS) is a crucial hormonal disorder globally affecting women with several other health issues such as acne, weight gain, infertility, etc., and thus emphasizes the need for early and accurate diagnosis and personalized health care. In this work we use the Convolutional Neural Networks, which extract features from ultrasonic images, bringing up an efficient way to categorize and understand PCOS. Beyond detection, the proposed approach integrates individual clinical data with PCOS ultrasounds, enabling personalized interventions with tailored health tips and dietary recommendations. The findings highlight the system’s reliability and efficiency, marking a significant advancement in PCOS understanding and management. The integrated Personalized Health Improvement Recommendation offers tailored health plans, exercise recommendations, and dietary plans, with follow-up routines enhancing continuous monitoring. The simulation results indicate $96 \%$ accuracy in PCOS detection. This dynamic approach is a boon to health management by giving recommendations with individual preferences and clinical data thus encouraging user interest and better health-care management.