Classification Prediction of Lung Cancer Based on Machine Learning Method

Dantong Li, Guixin Li, Shuang Li and Ashley Bang
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Dantong Li: Weifang Hospital of Traditional Chinese Medicine, China
Guixin Li: Weifang Hospital of Traditional Chinese Medicine, China
Shuang Li: Weifang Hospital of Traditional Chinese Medicine, China
Ashley Bang: The St. Nicholas School, Vietnam

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2024, vol. 19, issue 1, 1-12

Abstract: The K-nearest neighbor interpolation method was used to fill in missing data of five indicators of coronary heart disease, diabetes, total cholesterol, triglycerides, and albumin;, and the SMOTE algorithm was used to balance the number of variable indicators. The Relief-F algorithm was used to remove 18 variable indicators and retain 42 variable indicators. LASSO and ridge regression algorithms were used to remove eight variable indicators and retain 52 variable indicators; The prediction accuracy, recall, and AUC values of the linear kernel support vector machine model filtered using Relief-F and LASSO features are high, and the prediction results are optimal; The test result of random forest screened by Relief-F and LASSO features is better than that of the support vector machine model. It is concluded that the random forest model screened by Relief-F features is better as a prediction of lung cancer typing. The research results provide theoretical data support for predicting lung cancer classification using machine learning methods.

Date: 2024
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