1. A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach
- Author
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Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, and Yan Li
- Subjects
Machine learning ,Non-small lung cancer ,Prognosis ,Survival ,Prediction ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract Background Lung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients. Methods Laboratory indices of the NSCLC patients at their initial visit were collected for quality control and exploratory analysis. By comparing the levels of the above indices between the survival and death groups, the statistically significant indices were selected for subsequent machine learning modeling. Ten machine learning algorithms were then employed to develop the predictive models with survival and recurrence as outcomes, respectively. Moreover, regression models were constructed using the random survival forest algorithm by incorporating the survival time dimension. Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. Results 682 patients were enrolled according to the inclusion and exclusion criteria. The preliminary comparison results revealed that except for fast blood glucose, CD3 +T cell proportion, NK cell proportion, and CA72-4, there were significant statistical differences in other tumor markers, inflammation, metabolism, and immune-related indices between the survival and death groups (p
- Published
- 2024
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