Esophageal cancer risk prediction method based on SOM neural network and SVM

Abstract

The invention provides an esophageal cancer risk prediction method based on an SOM neural network and an SVM, and the method comprises the steps: firstly collecting M types of blood index informationand lifetime information of an esophageal cancer patient, and enabling the information to serve as an original data set; secondly, clustering the M blood indexes by using an SOM neural network to obtain a clustering result of the M blood indexes; performing regression verification on the clustering result by using a COX risk regression model to obtain N kinds of blood index information significantly related to the lifetime of the esophageal cancer patient; thirdly, finding a critical threshold value of the lifetime by drawing an ROC curve, and dividing the risk levels; and finally, optimizingSVM parameters by using a genetic algorithm, and selecting an RBF kernel function to establish an esophageal cancer risk prediction model. According to the method, a plurality of blood indexes remarkably related to the lifetime can be found at the same time through SOM neural network clustering and SVM model construction, and the risk level of the esophageal cancer is reasonably, conveniently andeffectively predicted.

Description

technical field [0001] The invention relates to the technical field of cancer risk assessment, in particular to an esophageal cancer risk prediction method based on SOM neural network and SVM. Background technique [0002] Cancer has always been one of the leading causes of death in both developed and developing countries, causing a huge social and economic burden. Esophageal cancer is one of the tumor types with high morbidity and mortality worldwide, and ranks sixth among the causes of tumor-related death. More than 300,000 people die from esophageal cancer every year in the world, and 90% of the cases are Esophageal squamous cell carcinoma. my country is one of the regions with a high incidence of esophageal cancer in the world, and esophageal cancer has become an important disease affecting the health of our people. [0003] On the one hand, with the advancement of science and technology and the innovation of medical technology, the treatment methods and concepts of es...

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Problems solved by technology

[0004] In the contemporary social environment, with the continuous expansion of medical data, traditional technologies and methods can no longer cope with the processing of large amounts of complex data.

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