An Optimized Approach for Detecting Osteoporosis Using Advanced Machine Learning and Genetic Algorithms

This project aims to optimize the detection of osteoporosis using advanced machine learning techniques and genetic algorithms. The dataset incorporates various factors influencing osteoporosis, including age, gender, hormonal changes, family history, race/ethnicity, body weight, calcium intake, vitamin D intake, physical activity, smoking, alcohol consumption, medical conditions, medications, and prior fractures. The target variable, "Osteoporosis," is classified into two categories: 0 (no osteoporosis) and 1 (osteoporosis). To develop a robust diagnostic model, algorithms such as Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machines (SVM) were applied. Additionally, genetic algorithms were employed to fine-tune the feature selection process, enhancing the model’s predictive accuracy. A user-friendly web application was developed using Flask, HTML, CSS, and JavaScript, enabling users to log in, register, and input their personal information for osteoporosis risk prediction. The platform helps users assess their risk of osteoporosis based on various lifestyle factors and medical conditions, providing personalized recommendations for prevention and management. This solution contributes to early diagnosis and better management of osteoporosis, potentially improving patients' quality of life and reducing healthcare costs.

Keywords: Osteoporosis Detection, Machine Learning, Genetic Algorithms, Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines (SVM), Flask, Predictive Modeling, Osteoporosis Risk Prediction, Feature Selection.