PREDICTIVE MACHINE LEARNING AND AI MODELS FOR RISK ASSESSMENT OF CHRONIC DISEASES

Osteoporosis refers to a metabolic bone disorder in which the bone mineral density is significantly reduced, leading to fragile bones susceptible to frequent fractures. Both osteoporosis and its precursor, osteopenia, being asymptomatic until a fracture occurs, frequently go undiagnosed. Early detection is therefore essential to prevent disease progression and reduce associated health risks. In this project, the application of machine learning and artificial intelligence models such as Random Forest, XGBoost, Support Vector Machines, and Neural Networks were implemented to evaluate bone health status based on lifestyle and health-related parameters. For the same, a dataset with general health metrics of 240 patients was considered and different models were developed to classify individuals as having normal bone density, osteopenia, or osteoporosis. Comparative assessment of the performance metrics of each of the models was done to identify the most accurate and robust approach. It was observed that the XGBoost algorithm worked best for this problem. The findings illustrate the practical value of machine learning techniques in enhancing early detection of disease and enabling timely intervention, thereby contributing to better management of bone health and effective early detection.