:: Volume 21, Issue 2 (6-2013) ::
Journal of Ilam University of Medical Sciences 2013, 21(2): 120-128 Back to browse issues page
Comparison of Artificial Neural Network and Cox Regression Models in Survival Prediction of Breast Cancer Patients
M Salehi * 1, Mr Gohari , N Vahabi , F Zayeri , Sh Yahyazadeh , M Kafashian
1- , salehi74@yahoo.com
Abstract:   (14661 Views)
Introduction: Nowadays, cancer diseases are the most important causes of death wor-ldwide and the breast cancer is the most important of them within women. Asses-sing the survival of the patients is one of the most important indices of controlling the cancer. This study aimed to make a compa-rison between the prediction of artificial neural networks (ANN) and Cox regression models for the breast cancer survival. Materials & Methods: The data of the sur-vival study gathered from 344 breast cancer patients between 2005 and 2012 that regist-ered at the Fayyazbakhsh hospital, Tehran, Iran. The status of survival was considered as a dependent variable. Area under rece-iver operative characteristic curve (AUR-OC) and classification accuracy were used for the comparison of artificial neural netw-orks and Cox regression models. Data anal-ysis was performed by R and Minitab sof-tware. Findings: The age of participants expres-sed as Mean ± SD, was 49.9±10.93 years and the median of survival was 44.6 mo-nths. Up to the end study, 45 (13.1) were died. Results showed that AUROC for AN-N and Cox regression were 87.6% and 75.4%, respectively. In addition the clas-sification accuracy of ANN and Cox regres-sion were calculated as 89.42 and 77.68, respectively. Discussion & Conclusion: According to the results, the total classification accuracy of the ANN was better than those of the Cox regression therefore, the ANN model is suggested to predict the survival status of breast cancer disease and also is suggested for diagnostic goals.
Keywords: breast cancer, survival, cox reg-ression, artificial neural networks
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Type of Study: Research | Subject: biostatistics
Received: 2013/08/3 | Accepted: 2013/08/6 | Published: 2013/10/15

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Volume 21, Issue 2 (6-2013) Back to browse issues page