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

The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature. We conducted a Medline literature search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes. We inc...

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Veröffentlicht in:	Journal of clinical epidemiology 2019-06, Vol.110, p.12-22
Hauptverfasser:	Christodoulou, Evangelia, Ma, Jie, Collins, Gary S., Steyerberg, Ewout W., Verbakel, Jan Y., Van Calster, Ben
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Area Under Curve Artificial intelligence Artificial neural networks AUC Bias Calibration Classification Clinical prediction models Confidence intervals Discriminant analysis Epidemiology Humans Learning algorithms Logistic Models Logistic regression Machine learning Mathematical models Medical research Modelling Models, Theoretical Neural networks Outcome Assessment, Health Care Prediction models Predictive Value of Tests Regression analysis Reporting Sensitivity and Specificity Statistical analysis Studies Supervised Machine Learning Support vector machines Systematic review
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