Presenting a Model for Detecting Osteoporosis In Active Older Men Using the Support Vector Machine Algorithm

Abstract

Background and Aims: Fractures due to osteoporosis impose high economic costs on patients and the health care system. Data mining has many applications in various fields, including medicine and sports, due to its ability to process large amounts of data and reduce detection time. Therefore, this study aims to provide a model for detecting osteoporosis in active older men using the support vector machine (SVM) algorithm. Methods: This is a development-applied study. Medical data of 652 patients were first examined. Of these, 108 active older men were selected including 58 healthy men, 33 with osteopenia, and 17 with osteoporosis. The SVM algorithm was used to differentiate them. MATLAB software version 2020 was also used for data analysis. Evaluation was performed using the confusion matrix and based on the accuracy and precision criteria. Results: Of 103 features related to sociodemographic information of participants, 8 features were selected as the inputs of the algorithm. The SVM algorithm could detect osteoporosis with 59.3% accuracy and 54.91% precision. Conclusion: By discovering hidden patterns and relationships in the data, the SVM algorithm can help improve the quality of diagnostic services for osteoporosis.