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Received: 20 Jan 2020 Accepted: 16 April 2020 Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for (TAN) when the precision of TAN is higher comparing to other methods. Conclusion: In this study, lactation duration, history of osteoporosis, calcium intake, immune-suppressor drugs, hyperlipidemia drugs, autoimmune diseases, number of pregnancies, hyperlipidemia, vitamin D, hyperparathyroidism, exercising during the week, anti-inflammatory drugs, thalassemia, waist disc, anti-coagulants drugs, hypothyroidism, hypertension drugs, history of surgery, diabetes and diabetes-related drugs were identified as important factors in relation to osteoporosis. These factors can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.

Osteoporosis Risk Prediction Using Data Mining Algorithms