Logistic big data prediction of gastric cancer

Abstract

In the era of big data, precision medicine for gastric cancer refers to the high throughput analysis of clinical data of patients with gastric cancer. The proposal of personalized diagnosis and treatment for each patient’s molecular biology and pathological characteristics, so as to achieve personalized precision medicine. In this paper, the prediction method is significance analysis and the statistical method of prediction model is logical analysis and discriminant analysis, and the significance test standard is p < 0.05. Logistic regression was used to identify and predict the prevalence of cancer patients, and the data used for prediction analysis were all from MS SQL database. The results showed that blood routine, blood biochemical and urine routine data can be used to distinguish cancer patients from healthy people. The cancer risk prediction model based on blood routine, blood biochemical and urine routine data can accurately target high-risk cancer people with an accuracy of 95.5%. In the era of big data, the accurate diagnosis and treatment strategy of gastric cancer can promote the aggregation, integration and sharing of gastric cancer related data.