The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.

Abstract

Background: Gastric cancer is one of the most common cancers. We can use AI for predictive models and help us in early detection and diagnosis.

Objective: This study examines the use of a neural network model to classify gastric cancer based on clinical, demographic and genetic data.

Methods: The data from the participants were divided into two subsets. 70% training data and 30% testing data. The neural network model has 12 input variables. Factors influencing a disease can be age, sex, family history, smoking, alcohol, Helicobacter pylori infection, food habits, diseases, endoscopic images, biopsy, CT scan, gene variants (TP53, KRAS, CDH1). The hyperbolic tangent activation function has four units in the hidden layer of a model. The output layer used a Softmax activation function and cross-entropy error function which predicted the presence of gastric cancer. The assessment was done on the predictors.

Results: The training and testing datasets showed 100% accuracy predicting gastric cancer in the model outputs. Age, gender, family history, infection with Helicobacter pylori, smoking, and drinking alcohol are the biggest predictors. Information from clinical diagnosis like endoscopic images, biopsy and CT scans helped the predictive model.

Conclusion: The neural network was able to perform well for gastric cancer predictions using multiple clinical and demographic factors, showing great utility. The outcomes for AI-based diagnostic tools look promising in cancer, however generalization needs to be confirmed using external datasets. The study shows how artificial intelligence can better precision medicine and cancer diagnosis.