• Gotlur Kalpana
• T.Kaivalya Sai
• T.Anoop Reddy

A chronic disorder called osteoporosis is characterized by a reduction in bone density, which raises the risk of fractures and poses serious public health issues. Based on clinical data, this study aims to classify osteoporosis illness using machine learning algorithms. We implemented and assessed four machine learning models: Decision Tree Classifier (DTC), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Logistic Regression (LR). These models' performance was evaluated using important criteria like recall, accuracy, and precision. The Decision Tree Classifier performed better (ie accuracy is 85.3%,precision is 84.6%, Recall is 87.4%,F1 score is 85.9%) than the other evaluated models, attaining the greatest classification accuracy. The Decision Tree Classifier appears to be a viable method for osteoporosis detection and classification, according to these findings, which could help with early diagnosis and individualized therapy planning. results in healthcare.

Osteoporosis Classification, Machine Learning, Decision Tree Classifier, Diagnostic Accuracy

"Leveraging Machine Learning for Effective Osteoporosis Detection and Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.a497-a502, February-2025, Available :http://www.jetir.org/papers/JETIR2502061.pdf

"Leveraging Machine Learning for Effective Osteoporosis Detection and Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppa497-a502, February-2025, Available at : http://www.jetir.org/papers/JETIR2502061.pdf