ارائه مدلی برای پیش‎‌بینی احتمال ابتلا به بیماری پوکی استخوان با استفاده از الگوریتم های درخت تصمیم

Background and purpose: Some diseases such as osteoporosis may have no symptom but suddenly cause fractures in different parts of body such as spine, chest, hands and legs, thereby resulting in very painful death in old people. According to a report by Iran’s ministry of health 4.6% of people aged 20 to 70 years in Iran are affected by osteoporosis in the spine. This study aimed at determining the factors influencing the incidence of osteoporosis and also providing a predictive model to speed up the detection and reduce diagnostic costs. Material and Methods: Data was collected by interviewing 670 patients in an orthopedic clinic. The information included demographic information, lifestyle and diseases, and the results of DEXA scan. In this paper, a new model based on the standard methodology CRISP is presented. In modeling, three known data mining methods, the CHAID, C5.0 decision tree, and neural network were used. For data analysis Celementine V.12.0 was used.. Results: In this study, for the first time in Iran, the characteristics affecting osteoporosis in patients has been studied. Using data mining techniques, influencing characteristics of the disease have been identified. According to the created decision tree, some rules are derived that can be used as a model for the prediction of patient’s status. Accuracy of built models using the algorithms C.5.0, CHAID, and neural networks were compared. Each algorithm was observed to act better in predicting osteoporosis in a specified group of people. Conclusion: The accuracy of artificial neural networks algorithm is higher than that of the decision tree algorithm. In this study the most affected factors on osteoporosis were detected. According to the created rules for a new instant with specified features, we can predict whether a patient will probably suffer from osteoporosis or not.