Osteoporosis Risk Prediction Using Data Mining Algorithms
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 th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Shahid Sadoughi University of Medical Sciences
2020-06-01
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| Series: | Journal of Community Health Research |
| Subjects: | |
| Online Access: | http://jhr.ssu.ac.ir/article-1-504-en.html |
| _version_ | 1811328662370254848 |
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| author | efat jabarpour amin Abedini abbasali keshtkar |
| author_facet | efat jabarpour amin Abedini abbasali keshtkar |
| author_sort | efat jabarpour |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T15:28:55Z |
| format | Article |
| id | doaj.art-a40342e19b984d9f9feea4f038fe24d6 |
| institution | Directory Open Access Journal |
| issn | 2322-5688 2345-2609 |
| language | English |
| last_indexed | 2024-04-13T15:28:55Z |
| publishDate | 2020-06-01 |
| publisher | Shahid Sadoughi University of Medical Sciences |
| record_format | Article |
| series | Journal of Community Health Research |
| spelling | doaj.art-a40342e19b984d9f9feea4f038fe24d62022-12-22T02:41:25ZengShahid Sadoughi University of Medical SciencesJournal of Community Health Research2322-56882345-26092020-06-01926980Osteoporosis Risk Prediction Using Data Mining Algorithmsefat jabarpour0amin Abedini1abbasali keshtkar2 1. Department of Industrial Engineering, School of Engineering, Payame Noor University, Tehran Shomal Branch, Tehran,Iran 2. Department of Computer Engineering, School of Electrical and Computer, Engineering Islamic Azad University, Qazvin Branch, Iran 3. Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 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.http://jhr.ssu.ac.ir/article-1-504-en.htmlosteoporosisdata miningsupport vector machinebayes network |
| spellingShingle | efat jabarpour amin Abedini abbasali keshtkar Osteoporosis Risk Prediction Using Data Mining Algorithms Journal of Community Health Research osteoporosis data mining support vector machine bayes network |
| title | Osteoporosis Risk Prediction Using Data Mining Algorithms |
| title_full | Osteoporosis Risk Prediction Using Data Mining Algorithms |
| title_fullStr | Osteoporosis Risk Prediction Using Data Mining Algorithms |
| title_full_unstemmed | Osteoporosis Risk Prediction Using Data Mining Algorithms |
| title_short | Osteoporosis Risk Prediction Using Data Mining Algorithms |
| title_sort | osteoporosis risk prediction using data mining algorithms |
| topic | osteoporosis data mining support vector machine bayes network |
| url | http://jhr.ssu.ac.ir/article-1-504-en.html |
| work_keys_str_mv | AT efatjabarpour osteoporosisriskpredictionusingdataminingalgorithms AT aminabedini osteoporosisriskpredictionusingdataminingalgorithms AT abbasalikeshtkar osteoporosisriskpredictionusingdataminingalgorithms |