A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

J Clin Epidemiol. 2019 Jun:110:12-22. doi: 10.1016/j.jclinepi.2019.02.004. Epub 2019 Feb 11.


Objectives: The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature.

Study design and setting: We conducted a Medline literature search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes.

Results: We included 71 of 927 studies. The median sample size was 1,250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and eight events per predictor (range 0.3-6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52-0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, -0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20-0.47) higher for ML.

Conclusion: We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.

Keywords: AUC; Calibration; Clinical prediction models; Logistic regression; Machine learning; Reporting.

Publication types

  • Comparative Study
  • Meta-Analysis
  • Research Support, Non-U.S. Gov't
  • Systematic Review

MeSH terms

  • Algorithms
  • Area Under Curve
  • Humans
  • Logistic Models*
  • Models, Theoretical*
  • Outcome Assessment, Health Care
  • Predictive Value of Tests
  • Sensitivity and Specificity
  • Supervised Machine Learning*