A Machine Learning-Based Survival Prediction Model For Esophageal Cancer

With the development of information technology and the accumulation of medical data,data analysis and modeling under computer assistance has become the current hot spot in esophageal cancer diagnosis and treatment technology.In this paper,esophageal cancer data are analyzed and processed based on machine learning methods,the potential relationships of indicators are mined,market models and prognostic indices are constructed,and survival prediction models are established.The main contents are as following.For survival risk prediction of esophageal cancer,traditional statistical analysis methods have certain complexity and limitations in extracting correlated multiple indicators.Survival risk level prediction models based on Self-Organizing Map(SOM)neural network and improved Support Vector Machine(SVM)are proposed.Based on the SOM clustering approach,indicators significantly related to patient survival are unsupervisedly extracted,and the survival risk level of patients is further predicted based on the improved SVM.The constructed SOMSVM model provides a method for clinical prognosis of esophageal cancer.Due to the complexity of traditional methods of determining the degree of differentiation of esophageal cancer tumors-it is necessary to observe the cell morphology under a microscope and give the degree of differentiation in combination with manual experience.A machine learning-based method for tumor differentiation degree identification is proposed.A marker model of esophageal cancer tumor differentiation degree is constructed based on multiple logistic regression and Relief F algorithm,and a prediction model of esophageal cancer tumor differentiation degree is constructed based on machine learning classification algorithm.Differentiation status is effectively judged by the constructed differentiation degree marker model and prediction model of esophageal cancer.Since the differentiation degree of tumor can effectively reflect the prognosis of patients,the constructed prediction model of esophageal cancer tumor differentiation degree further provides ideas for clinical diagnosis and prognosis analysis of esophageal cancer.Since the TNM staging system does not always accurately reflect the prognostic status of patient survival in the prognostic analysis of esophageal cancer with high pathological complexity.A five-year survival prediction model for esophageal cancer based on Prognostic Index(PI)and Sparrow Search Algorithm-Support Vector Machine(SSA-SVM)is proposed.The prognostic index(PI)for esophageal cancer is constructed based on univariate analysis and multivariate logistic regression.The PI is classified into four stages.A five-year survival prediction model for esophageal cancer is constructed based on SSA-SVM.The constructed PI staging system is superior to the TNM staging system,and the effective judgment of esophageal cancer prognosis is achieved.The five-year survival of esophageal cancer patients is effectively predicted by the constructed five-year survival prediction model for esophageal cancer.It provides a means for the prognosis and diagnosis of esophageal cancer.In this paper,the SOM-SVM esophageal cancer survival risk level prediction model,the esophageal cancer differentiation degree marker model and prediction model,the esophageal cancer PI staging prognostic system,and the PI-SSA-SVM esophageal cancer survival prediction model are constructed based on the machine learning approach.The effective inference of esophageal cancer prognosis is achieved by machine learning method,which provides a reference for intelligent treatment of esophageal cancer.