Early Detection of Osteoporosis using Machine Learning and Biomarkers

Early detection osteoporosis is crucial to prevent complications, but current screening methods can be expensive and inaccessible. This paper presents a novel approach for the early detection of osteoporosis using machine learning and simulation tool based on the biomarker. We have developed a predictive model that analyses patient data, such as Age, medical factors including Bone mass density and biomarkers like Osteocalcin and Parathyroid Hormone to identify individuals at different levels of risks of developing osteoporosis. The paper also includes the Osteocalcin parameter, a new method, which makes it better than the existing system and very crucial for the detection of Osteoporosis. The proposed system was trained using a Support Vector Machine (SVM) algorithm, a powerful machine learning technique that can effectively handle complex, high-dimensional data. Additionally, we have used a simulating tool to simulate the detection and measurement of blood protein Osteocalcin biomarker level. By combining machine learning predictions with simulation based biochemical analysis, our proposed framework offers a comprehensive approach to osteoporosis detection.