An application to detect osteoporosis using ensemble Machine learning with hyperparameter tuning and model interpretability

Millions of people worldwide suffer greatly from osteoporosis, a chronic bone disease marked by decreased bone mass and structural degradation. Timely intervention and therapy of osteoporosis depend heavily on accurate early osteoporosis prediction. In the proposed method, use a chronic dataset of patient characteristics and risk variables to present a machine learning framework for osteoporosis prediction. Class imbalance is handled by the pipeline by utilizing synthetic minority over-sampling technique (SMOTE) and other data preprocessing techniques including scaling and normalization. Then, the data was split in an 80:20 ratio and seven features were selected by mutual information. Using an ensemble learning technique and also adjusted the hyperparameters of several classification algorithms such as random forest, k-nearest neighbors, support vector machine, XGBoost and logistic regression. XGBoost, the top-performing algorithm has an AUC score of 81.08%, showing excellent classification performance. Furthermore, the interpretability of the model was improved through the utilization of shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) by the XGBoost. This facilitated a more profound comprehension of the fundamental elements propelling the prediction. At last, develops a web interface where patients can know about their own condition by it. According to the work, the suggested framework is a useful tool for osteoporosis early prognosis which could help medical practitioners make treatment decisions.