@article{ref1,
title="Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries",
journal="Journal of critical care",
year="2019",
author="Feng, Jin-Zhou and Wang, Yu and Peng, Jin and Sun, Ming-Wei and Zeng, Jun and Jiang, Hua",
volume="54",
number="",
pages="110-116",
abstract="PURPOSE: To compare twenty-two machine learning (ML) models against logistic regression on survival prediction in severe traumatic brain injury (STBI) patients in a single center study. <br><br>MATERIALS AND METHODS: Data was collected from STBI patients admitted to the Sichuan Provincial People's Hospital between December 2009 and November 2011. Twenty-two machine learning (ML) models were tested, and their predictive performance compared with logistic regression (LR) model. Receiver operating characteristics (ROC), area under curve (AUC), accuracy, F-score, precision, recall and Decision Curve Analysis (DCA) were used as performance metrics. <br><br>RESULTS: A total of 117 patients were enrolled. AUC of all ML models ranged from 86.3% to 94%. AUC of LR was 83%, and accuracy was 88%. The AUC of Cubic SVM, Quadratic SVM and Linear SVM were higher than that of LR. The precision ratio of LR was 95% and recall ratio was 91%, both were lower than most ML models. The F-Score of LR was 0.93, which was only slightly better than that of Linear Discriminant and Quadratic Discriminant. <br><br>CONCLUSIONS: The twenty-two ML models selected have capabilities comparable to classical LR model for outcome prediction in STBI patients. Of these, Cubic SVM, Quadratic SVM, Linear SVM performed significantly better than LR.<br><br>Copyright © 2019. Published by Elsevier Inc.<p /> <p>Language: en</p>",
language="en",
issn="0883-9441",
doi="10.1016/j.jcrc.2019.08.010",
url="http://dx.doi.org/10.1016/j.jcrc.2019.08.010"
}