Recurrent Gastric Cancer Prediction Using Randomized Search Cv Optimizer

Gastric cancer has the second-highest mortality rate and the fourth-highest incidence of malignant tumors worldwide. Early stomach cancer has a fair prognosis, but the symptoms are unusual and the signs are not immediately apparent. The goal of the project was to use patient clinicopathological data and machine learning techniques to increase the precision of a prediction model for the emergence of gastric cancer. The current study proposes the first four parameters. By comparing the performance of Gradient Boosted Decision Tree, Random Forest, Decision Tree, and Gradient Boosting Machine, Logistic Regression, it was shown that machine learning approaches can be used to estimate the repetition of patients with malignant after such operation. The parameters influencing postsurgical repetition of stomach cancer have been proposed in the present study. In this case, Random Forest is enhanced using the Random Search Cross Validation optimizer to increase the model's performance in predicting cancer. Random Search Cross Validation performs a random search over parameters, sampling each setting from a range of potential parameter values. The dataset contains 2012 patient records as well as 51 characteristics