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Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study

After determining a practical set, we ran RF, SVM, and KNN algorithms for comparison. All models were trained and evaluated using the default parameters of the classifiers. The XGBoost base model used the following default hyperparameters: scale_pos_weight = 1, n_estimators = 500, max_depth = 6, eta = 0.3, gamma = 0, reg_lambda = 1.0, early_stopping_rounds = 5, and eval_metric = 'logloss'.

Anjun Chen, Erman Wu, Ran Huang, Bairong Shen, Ruobing Han, Jian Wen, Zhiyong Zhang, Qinghua Li

JMIR AI 2024;3:e56590

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