Estimation of Bone Mineral Density using Machine Learning and SHapley Additive exPlanations

Osteoporosis is a worldwide health issue marked by decreased bone density and degradation of bone tissue, which raises the risk of fractures. Early diagnosis of low hone mineral density (BMD) is crucial in reducing risks by providing appropriate treatment or prevention methods. However, the most common method of measuring BMD is the Dual-energy x-ray absorptiometry, which might not he affordable or accessible to many patients. This study proposes using machine learning methods to predict BMD through anthropometric measurements, anamnesis, age, and sex. A dataset containing 905 patients with their corresponding features and BMD values was also introduced. Different regression algorithms were evaluated, and the model predictions were interpreted using SHapley Additive exPlanations. The approach demonstrated good performance, with an average mean absolute error and mean absolute percentage error of 0.0771 g/cm(2) and 6.34%, respectively. As a result, this proposed method can potentially become a tool for healthcare professionals to predict BMD in a cost-effective and accessible manner.