Volume 9, Issue 2 (Apr- June 2020)
JCHR 2020, 9(2): 69-80 |
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jabarpour E, Abedini A, keshtkar A. Osteoporosis Risk Prediction Using Data Mining Algorithms. JCHR 2020; 9 (2) :69-80
URL: http://jhr.ssu.ac.ir/article-1-504-en.html
URL: http://jhr.ssu.ac.ir/article-1-504-en.html
1- 1. Department of Industrial Engineering, School of Engineering, Payame Noor University, Tehran Shomal Branch, Tehran,Iran
2- 2. Department of Computer Engineering, School of Electrical and Computer, Engineering Islamic Azad University, Qazvin Branch, Iran ,aminabedini.ai@gmail.com
3- 3. Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2- 2. Department of Computer Engineering, School of Electrical and Computer, Engineering Islamic Azad University, Qazvin Branch, Iran ,
3- 3. Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (3767 Views)
Abstract
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: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.
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: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.
Review: Research |
Subject:
Health information management
Received: 2019/02/16 | Accepted: 2020/06/29 | Published: 2020/06/29
Received: 2019/02/16 | Accepted: 2020/06/29 | Published: 2020/06/29
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