این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Acta Medica Iranica، جلد ۶۱، شماره ۴، صفحات ۲۲۹-۲۳۷
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Identifying the Most Important Factors in Determining the Osteoporosis in Women Using Data Mining Techniques
چکیده انگلیسی مقاله Osteoporosis is one of the primary causes of disability and mortality in the elderly. If osteoporosis's significant features can be identified, the risk of developing this disease will be reduced. In recent years, data mining approaches have become a suitable tool for medical researchers. This study applied data mining methods to identify osteoporosis’s significant features. This study applied data from women having osteoporosis or osteopenia in the period 2011-2019 in the Osteoporosis Diagnosis Center, Isfahan, Iran. Data mining methods such as linear regression, naïve bayes, decision tree, support vector machine, random forest, and neural network were implemented on the dataset. This study consisted of 8258 patients’ information, of which 1482 had osteoporosis. The results showed that the support vector machine, decision tree, neural network are the best method based on accuracy, precision, and AUC measures. Six candidate features were age, weight, back pain, low activity, menopause date, and previous fracture. Support vector machine, decision tree, and neural network are the best candidate techniques for predicting osteoporosis. Thin older people are more at risk of osteoporosis than other people. Yet, people with middleweight and middle age are at lower risk of osteoporosis.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله | Mohammadreza Salamat
Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Amirhossein salamat
Research and Development Division, Osteoporosis Diagnosis Center, Isfahan, Iran


| Mohammad Sattari
Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran


| Mehdi Asgari
Department of Nursing, School of Nursing, Larestan University of Medical Sciences, Larestan, Iran
نشانی اینترنتی https://acta.tums.ac.ir/index.php/acta/article/view/9377
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات