Survival prediction of gastric cancer patients by Artificial Neural Network model

Jamshid Yazdani Charati, Ghasem Janbabaei, Nadia Alipour, Soraya Mohammadi, Somayeh Ghorbani Gholiabad, Afsaneh Fendereski

Abstract


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Aim: This study aims to predict survival rate of gastric cancer patients and identify the effective factors related to it, using artificial
neural network model.
Background: Gastric cancer is the most deadly disease in north and northeast provinces of Iran. A total of 430 patients with gastric
cancer who referred to Baghban clinic in Sari, from early November 2006 to late October 2013 were followed.
Methods: A historical cohort of patients who referred to Baghban Clinic, the cancer research center of Mazandaran University of
Medical Sciences in Sari, from early November 2006 to late October 2013 was studied. Three groups of variables (demographic,
biological and socio-economic) were studied. Survival rate and effective factors on survival time were calculated using Kaplan-Meier
methods and artificial neural networks and the best network structure were chosen using the mean square error and ROC curve. All
analyses were performed using SPSS v.18.0 and the level of significance was selected ?=0.05.
Results: In this research, the median survival time was 19±2.04 months. The 1 to 5-year survival rates for patients were 0.64, 0.44,
0.34, 0.24 and 0.19, respectively. The percentage of right predictions of the selected network and the area under the ROC curve were
92% and 94%, respectively. According to the results, the type of treatment, metastasis, stage of disease, histology grade, histology type
and the age of diagnosis were effective factors on survival period.
Conclusion: the 5 years survival rate of gastric cancer patients in Mazandaran is lower than other provinces which could be due to the
delay in diagnosis or patient’s referral. Therefore, the use of screening methods and early diagnosis could be influential for improving
survival rate of these patients.
Keywords: Gastric cancer, Survival analysis, Artificial neural network.
(Please cite as: Yazdani Charati J, Janbabaei G, Alipour N, Mohammadi S, Ghorbani Gholiabad S, Fendereski A.
predicting survival of gastric cancer patients by Artificial Neural Network model. Gastroenterol Hepatol Bed Bench
2018;11(2):110-117).


 


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DOI: http://dx.doi.org/10.22037/ghfbb.v0i0.1246

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