Selected article for: "final model and model variable"
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_10
    Snippet: In terms of feature selection, recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. The basic idea was that given a specified external learning algorithm, the prediction accuracy of all subsets of variable combinations were calculated through RFE, and number of the subset with the highest prediction accuracy was chosen as the optimal number. Then, the optimal number was used in RFE as the .....
    Document: In terms of feature selection, recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. The basic idea was that given a specified external learning algorithm, the prediction accuracy of all subsets of variable combinations were calculated through RFE, and number of the subset with the highest prediction accuracy was chosen as the optimal number. Then, the optimal number was used in RFE as the parameter to determine the entry predictor of the final model. In this research, the decision tree was used to select the predictors into the final model. Also, the Pearson correlation coefficient was used to analyze the variable correlation of the factors in model and the results were shown using a heat map. To explore the most important factors, we reduced the optimal number to a smaller one and repeated the above process. The co-existing factors were chosen for further discussion.

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