Selected article for: "accuracy score and logistic regression model"
Author: Zhao, Chun-Hong; Wu, Hui-Tao; Che, He-Bin; Song, Ya-Nan; Zhao, Yu-Zhuo; Li, Kai-Yuan; Xiao, Hong-Ju; Zhai, Yong-Zhi; Liu, Xin; Lu, Hong-Xi; Li, Tan-Shi
Title: Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study
  • Document date: 2020_3_5
    • ID: tk3861u0_24
    Snippet: In the methodological part, when 15 variables were selected, the best results were obtained from the logistic regression model and the bagging model. Logistic regression had the highest accuracy (0.951), while the bagging model had the highest AUC score (0.885). Given the clinical application of the model, the clearer the focus on the indicators, the better it could help doctors make decisions. In addition, if more indicators are involved, it is .....
    Document: In the methodological part, when 15 variables were selected, the best results were obtained from the logistic regression model and the bagging model. Logistic regression had the highest accuracy (0.951), while the bagging model had the highest AUC score (0.885). Given the clinical application of the model, the clearer the focus on the indicators, the better it could help doctors make decisions. In addition, if more indicators are involved, it is not always possible to ensure that every factor can be obtained in a short time. Therefore, in the case that the performance of the model does not change much, we believe that the model with fewer factors is more conducive in the clinic. The model may be used to predict, verify and improve future clinical medical practices.

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