Identification Method For Early Lung Canceration Based On Statistical Learning

Lung cancer is one of the malignant tumors with the fastest growth rate of morbidity and mortality,and it is the greatest threat to human health and life.As an important part of machine learning,statistical learning has developed rapidly in recent years on the basis of big data.Some of these classical methods can be used as tools to identify lung cancer.This article reviews the history and current status of lung cancer diagnosis,introduces and discusses the background of the artificial neural network models and the decision tree model,and the modeling and prediction of lung cancer data.The modeling process uses two mainstream methods in the current machine learning:BP neural network model and decision tree model.In the BP neural network model algorithm,the obtained samples are randomly divided into training set and prediction set at a certain proportion.Meanwhile,the artificial neural network model is trained and the appropriate hidden layer neuron number is selected.Then we use the prediction set to test the model's ability to distinguish between lung cancer patients and normal people by modifying the relevant parameters in the model until the most suitable neural network model is determined.In the decision tree section,we select the appropriate variables and divide the data into two or more more-purity data sets according to the value conditions until the preset conditions are reached.And the appropriate pruning should be chose to prevent overfitting to determine the optimal model.Finally,the article summarizes two models and elaborates the important mission of machine learning methods in the field of cancer diagnosis.