Volume 12, Number 2 (Vol 12, No 2 2016)                   irje 2016, 12(2): 49-57 | Back to browse issues page


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Zayeri F, Seyedagha S, Aghamolaie H, Boroumand F, Yavari P. Comparison of the Logistic Regression and Classification Tree Models in Determining the Risk Factors and Prediction of Breast Cancer . irje. 2016; 12 (2) :49-57
URL: http://irje.tums.ac.ir/article-1-5516-en.html

1- Associate Professor, Department of Biostatistics, Shahid Beheshti University of Medical Sciences, Tehran, Iran Associate Professor, Department of Biostatistics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Students’ Research Committee, Tehran, Iran Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Students’ Research Committee, Tehran, Iran , hosseinseyedagha@gmail.com
3- Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Department of Health and Community Medicine, Medical School, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Health and Community Medicine, Medical School, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract:   (4689 Views)

Background and Objectives: Breast cancer is one of the most common malignancies in women which accounts for the highest number of deaths after lung cancer. The aim of the current study was to compare the logistic regression and classification tree models in determining the risk factors and prediction of breast cancer.

Methods: We used from the data of a case-control study conducted on 303 patients with breast cancer and 303 controls. In the first step, we included 16 potential risk factors of breast cancer in both the logistic regression and classification tree models. Then, the area under the ROC curve (AUC), sensitivity, and specificity indexes were used for comparing these models.

Results: From 16 variables included in the models, 5 variables were statistically significant in both models. Sensitivity, specificity, and AUC was 71%, 69%, and 74.7% for the logistic regression and 63.3%, 68.8%, and 71.1% for the classification tree, respectively.

Conclusion: The obtained results suggest that the classification tree has more power for separating patients from healthy people. Menopausal status, number of breast cancer cases in the family, and maternal age at the first live birth were significant indicators in both models.

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Type of Study: Research | Subject: General
Received: 2016/08/7 | Accepted: 2016/08/7 | Published: 2016/08/7

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