Modeling of Health Research Center, Department of Biostatistics and Epidemiology, School of Health, Kerman University of Medical Sciences, Kerman, Iran.
Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for predicting survival of gastric cancer patients in Iran.
korhani Kangi, A., & Bahrampour, A. (2018). Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks. Asian Pacific Journal of Cancer Prevention, 19(2), 487-490. doi: 10.22034/APJCP.2018.19.2.487
MLA
Azam korhani Kangi; Abbas Bahrampour. "Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks". Asian Pacific Journal of Cancer Prevention, 19, 2, 2018, 487-490. doi: 10.22034/APJCP.2018.19.2.487
HARVARD
korhani Kangi, A., Bahrampour, A. (2018). 'Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks', Asian Pacific Journal of Cancer Prevention, 19(2), pp. 487-490. doi: 10.22034/APJCP.2018.19.2.487
VANCOUVER
korhani Kangi, A., Bahrampour, A. Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks. Asian Pacific Journal of Cancer Prevention, 2018; 19(2): 487-490. doi: 10.22034/APJCP.2018.19.2.487
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