Using RNN Artificial Neural Network to Predict the Occurrence of Gastric Cancer in the Future of the World

Abstract

Gastric cancer is an important health problem and is the fourth most common cancer and the second leading cause of cancer-related deaths worldwide. The incidence of stomach cancer is increasing and it can be dealt with using new methods in prediction and diagnosis. Our goal is to implement an artificial neural network to predict new cancer cases. Gastric cancer is anatomically divided into true gastric adenocarcinomas (non-cardiac gastric cancers) and gastric-esophageal- connective cancer (adenocardia (cardiac) gastric cancers). We use MATLAB R2018 software (MathWorks) to implement an artificial neural network. We used. The data were repeatedly and randomly divided into training (70%) and validation (30%) subsets. Our predictions emphasize the need for detailed studies on the risk factors associated with gastric cell carcinoma to reduce the incidence and has also provided an accuracy of about 99.998%.

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