Volume 17, Issue 1 (5-2018)
TB 2018, 17(1): 14-23 |
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askarishahi M, ghasemi N, Fallahzade H, Afkhami Ardekani M, AfkhamiArdekani A. Comparing of Decision Tree with Logistic Regression Model in Evaluating Osteoporosis. TB 2018; 17 (1) :14-23
URL: http://tbj.ssu.ac.ir/article-1-1209-en.html
URL: http://tbj.ssu.ac.ir/article-1-1209-en.html
Mohsen Askarishahi 
, Nasime Ghasemi * , Hossein Fallahzade , Mohammad Afkhami Ardekani , Arezoo AfkhamiArdekani
, Nasime Ghasemi * , Hossein Fallahzade , Mohammad Afkhami Ardekani , Arezoo AfkhamiArdekani
University , moasbio@gmail.com
Abstract: (3030 Views)
Introduction: Early detection of osteoporosis is a key to preventing of it; but recognition, without the use of appropriate diagnostic methods, due to the complexity of risk factors and gradual bone loss process, is problem. The purpose of this study is to develop and efficiency evaluation a predictive model of osteoporosis using decision tree technique as a diagnostic method based on available risk factors; thereby to identify individuals at risk for preventive activities.
Methods: In this study used data from 131 women aged 20 – 40 years. Response variable was amount of BMD (t-score) L1-L4 lumbar region that divided on two group, normal (t-score>= -1) and at risk of osteoporosis (t-score< -1). To determine risk factors of osteoporosis used from decision tree model with method of k- fold cross validation k=4 and logistic regression .To assess the accuracy prediction of two model, the area under receiver operative characteristic curves (AUROC) was used. Data analysis was performed by R software.
Results: Three variables number of pregnancies, BMI and calcium levels as risk factors for osteoporosis were obtained from the decision tree model and Area under receiver operative characteristic decision tree and logistic regression, respectively 0.665 and 0.686 were obtained.
Conclusion: Area under receiver operative characteristic curve showed advantage superiority of logistic regression that according to advantages of the decision tree applying simultaneously of two models is recommended.
Methods: In this study used data from 131 women aged 20 – 40 years. Response variable was amount of BMD (t-score) L1-L4 lumbar region that divided on two group, normal (t-score>= -1) and at risk of osteoporosis (t-score< -1). To determine risk factors of osteoporosis used from decision tree model with method of k- fold cross validation k=4 and logistic regression .To assess the accuracy prediction of two model, the area under receiver operative characteristic curves (AUROC) was used. Data analysis was performed by R software.
Results: Three variables number of pregnancies, BMI and calcium levels as risk factors for osteoporosis were obtained from the decision tree model and Area under receiver operative characteristic decision tree and logistic regression, respectively 0.665 and 0.686 were obtained.
Conclusion: Area under receiver operative characteristic curve showed advantage superiority of logistic regression that according to advantages of the decision tree applying simultaneously of two models is recommended.
Keywords: Area under receiver operative characteristic, Decision Tree, Osteoporosis, Regression Logistic
Type of Study: Research |
Subject:
Special
Received: 2014/11/22 | Accepted: 2015/01/11 | Published: 2018/05/15
Received: 2014/11/22 | Accepted: 2015/01/11 | Published: 2018/05/15
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