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_11
Snippet: We compared the performance of logistic regression, random forest, adaboost and bagging with the same predictors selected above. Accuracy, F1-score, precision, sensitivity and the area under receiver operator characteristic curves (ROC-AUC) were used as criteria for judging model performance. The cohort was split as training and testing set at a ratio of 7:3. The training coefficient of logistic regression was C = 0.01 and the penalty was set to .....
Document: We compared the performance of logistic regression, random forest, adaboost and bagging with the same predictors selected above. Accuracy, F1-score, precision, sensitivity and the area under receiver operator characteristic curves (ROC-AUC) were used as criteria for judging model performance. The cohort was split as training and testing set at a ratio of 7:3. The training coefficient of logistic regression was C = 0.01 and the penalty was set to L2. The base estimator of bagging was decision tree classifier and the training loss criterion was set to entropy. A total of 1000 trainings were performed. The models with good performance were cross verified by ten folds. For validation, we further collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance was also evaluated by accuracy, F1-score, precision, sensitivity, and ROC-AUC. The descriptive baseline analysis and hypothesis testing were done in IBM SPSS Statistics for Windows (Version 19.0. Armonk, NY: IBM Corp., USA), and the rest of the process was done in Python 3.6.1 (https://www.python.org).
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