عنوان انگلیسی مقاله:
Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries
ترجمه فارسی عنوان مقاله:
مقایسه رگرسیون لجستیک و الگوریتم های یادگیری ماشین در پیش بینی بقا از صدمات مغزی آسیب زا
Sciencedirect - Elsevier - Journal of Critical Care, 54 (2019) 110-116: doi:10:1016/j:jcrc:2019:08:010
Jin-zhou Feng a,b,1, YuWang a,b,1, Jin Peng a,b,c, Ming-wei Suna, Jun Zeng a,b, Hua Jiang a,b,⁎
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.
Materials andmethods: Datawas collected fromSTBI patients admitted to the Sichuan Provincial PeoplesHospital
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.
Results: A total of 117 patientswere enrolled. AUC of all ML models ranged from86.3% to 94%. AUC of LRwas 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.
Conclusions: The twenty-twoMLmodels 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
Keywords: Traumatic brain injury | Machine learning | Logistic regression | Survival prediction | Support vector machine | Critical illness