Objective The aim was to assess the risk of non-small cell lung cancer(NSCLC)by analyz-ing clinical indicators using machine learning methods.Methods In this study,we retrospectively analyzed the demographic characteristics and laboratory test results of 369 patients in Shaoyang Central Hospital to investigate the relationship between each clinical indicator and NSCLC.Firstly,one-way logistic regression analysis was performed,meanwhile,variable importance analysis was carried out using random forest classifier to identify the most influential indicators for early cancer risk assessment,and then least absolute shrinkage and selection opera-tor(LASSO)regression was performed to further screen the variables.)regression was performed to further screen the variables.Finally,the Logistic regression method was used to construct the nomogram,and the accu-racy and reliability of the model were further verified by using the receiver operating characteristic curve(ROC)and decision curve analysis(DCA)in the training and validation sets.Results Lasso regression and logistic re-gression analyses of the assessed indicators found that BMI(P＜0.001,95％CI=1.15-1.35),SKA1(P＜0.001,95％CI=1.17-1.38),SCC(P＜0.001,95％CI=2.42-7.51),CA242(P＜0.001,95％CI=1.07-1.28)and gender(P＜0.05,95％CI=0.26-0.91)were important indicators for assessing early cancer risk.In addition,the model was shown to have high accuracy and clinical applicability by measuring AUC,cali-bration curves and DCA curves in the training and test sets.Conclusion In this study,clinical indicators were ana-lyzed by machine learning algorithms,which can effectively assess the risk of non-small cell lung cancer.BMI,SKA1,SCC,CA242,and gender were found to be the indicators that had a significant effect on the risk assessment of non-small cell lung cancer,and thus can be used as important references to screen early stage cancers.
维普
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