Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.
BACKGROUND
Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn.


METHODS
An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index.


RESULTS
All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability.


CONCLUSIONS
The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts.
Centre for Health Informatics, Institute of Population Health, University of Manchester, UK. Electronic address: neophytos.stylianou@postgrad.manchester.ac.uk.
Neophytos
N
Stylianou
Centre for Health Informatics, Institute of Population Health, University of Manchester, UK.
Artur
A
Akbarov
Centre for Health Informatics, Institute of Population Health, University of Manchester, UK.
Evangelos
E
Kontopantelis
Centre for Health Informatics, Institute of Population Health, University of Manchester, UK.
Iain
I
Buchan
University Hospital South Manchester, Greater Manchester, UK.
Ken W
KW
Dunn
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D001499:Bayes Theorem; D001830:Body Surface Area; D002056:Burns / Q000401:mortality* / Q000473:pathology; D002648:Child; D002675:Child, Preschool; D003661:Decision Support Techniques; D004739:England; D005260:Female; D006801:Humans; D007223:Infant; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012372:ROC Curve; D012042:Registries; D018570:Risk Assessment / Q000379:methods; D015208:Smoke Inhalation Injury / Q000401:mortality*; D012984:Software; D060388:Support Vector Machine; D014852:Wales; D055815:Young Adult
Burn; Clinical prediction; Machine learning; Mortality