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Classification Prediction of Lung Cancer Based on Machine Learning Method

Classification Prediction of Lung Cancer Based on Machine Learning Method

Dantong Li, Guixin Li, Shuang Li, Ashley Bang
Copyright: © 2024 |Volume: 19 |Issue: 1 |Pages: 12
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9798369324707|DOI: 10.4018/IJHISI.333631
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MLA

Li, Dantong, et al. "Classification Prediction of Lung Cancer Based on Machine Learning Method." IJHISI vol.19, no.1 2024: pp.1-12. http://doi.org/10.4018/IJHISI.333631

APA

Li, D., Li, G., Li, S., & Bang, A. (2024). Classification Prediction of Lung Cancer Based on Machine Learning Method. International Journal of Healthcare Information Systems and Informatics (IJHISI), 19(1), 1-12. http://doi.org/10.4018/IJHISI.333631

Chicago

Li, Dantong, et al. "Classification Prediction of Lung Cancer Based on Machine Learning Method," International Journal of Healthcare Information Systems and Informatics (IJHISI) 19, no.1: 1-12. http://doi.org/10.4018/IJHISI.333631

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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.