SMART DIAGNOSTIC SYSTEM FOR HEALTH RISK ASSESSMENT
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A prevalent hormonal condition that affects women, polycystic ovarian disease (PCOD) must be detected early in order to be effectively treated. WomenCare, a web-based PCOD risk prediction system, is presented in this project. It uses a Logistic Regression machine learning model to assess PCOD risk based on clinical and lifestyle factors, including age, BMI, menstrual cycle pattern, weight gain, hair growth, acne, food, and exercise habits. A Flask-based online application that offers secure user login, real-time prediction, automatic BMI calculation, and downloadable PDF health reports is used to deploy the model, which is trained using a balanced class-weight strategy toincrease prediction reliability. To increase interpretability, the system divides users into Low, Medium, and High risk categories and offers risk explanations. Although it is not a replacement for a professional diagnosis, Women Care serves as an early screening and awareness tool to help women monitor their health and seek prompt medical treatment
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J, C. (2026). Smart Diagnostic System for Health Risk Assessment. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.245
J, Chitrakala. "Smart Diagnostic System for Health Risk Assessment." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.245.
J, Chitrakala. "Smart Diagnostic System for Health Risk Assessment." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.245.
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