Investigating the Factors Associated with Gastric Cancer by Neural Network Approach and Multiple Logistic Regression: A Case-Control

Document Type : Original Article (s)

Authors

1 MSc of Biostatistics, Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran

2 Assistant Professor, Department of Mathematics, Campus of Bijar, University of Kurdistan, Sanandaj, Iran

3 Assistant Professor, Department of Mathematics, University of Kurdistan, Sanandaj, Iran

4 MSc of Epidemiology, Infectious Diseases Research Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran

Abstract

Background: Stomach cancer is the fifth most common disease and the third cause of death in the world. Therefore, in order to prevent and reduce the incidence of stomach cancer, factors related to logistic regression and neural network models were investigated.
Methods: In this study, a survey was conducted on 1,170 people as (n = 390) cases and (n = 780) controls. The data collection tool was based on the researcher's checklist. The samples were selected by available sampling method and their information was collected by face-to-face and telephone interviews. The fitting power in the logistic regression model and neural network was compared with receiver function characteristic curve (AUROC), sensitivity and specificity. By introducing the superior model, significant and related factors with stomach cancer were reported.
Findings: The results showed that the accuracy, sensitivity and specificity of the neural network were 96.4%, 93.7% and 81.9%, respectively. But the accuracy, sensitivity and specificity of the logistic regression model were reported as 95.9%, 91.1% and 84.4%, respectively. The neural network model indicates the variables of age (0.646), fruit consumption (0.713), history of self-medication (0.652), history of gastric ulcer (0.734), family history of cancer (0.852) and Family history of stomach cancer (0.836) were associated with the incidence of stomach cancer.
Conclusion: Considering that in the present study, the fit of the neural network was superior to logistic regression and it does not need any special assumptions, so it is suggested to the researchers that the neural network model can be preferred over logistic regression.

Keywords

References

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Journal of Isfahan Medical School
Volume 40, Issue 693
1st Week,, January
January and February 2023
Pages 880-889

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History

  • Receive Date: 17 April 1401
  • Revise Date: 18 April 1401
  • Accept Date: 19 September 1401

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