Gradient boosting approaches can outperform logistic regression for risk prediction in cutaneous allergy

Abstract

Background: Contact allergy is a major clinical and public health challenge. It is important to identify individuals who are at risk and perform patch testing to identify relevant allergens. Predicting clinical risk on the basis of input parameters is common in clinical medicine and traditionally has been achieved with linear models.

Objectives: We hypothesized that the risk of a clinically relevant positive patch test could be predicted according to clinical and demographic parameters.

Methods: We compared the predictive accuracy of logistic regression with more sophisticated machine learning approaches such as gradient boosting, in the prediction of patch testing results.

Results: We found that both logistic regression and more sophisticated machine learning approaches were able to predict the risk of positive patch tests. For certain predictions, including the overall risk of a clinically relevant positive patch test, gradient boosting approaches can outperform logistic regression.

Conclusions: These findings suggest that complex nonlinear interactions between input variables are relevant in risk prediction. While a risk prediction model cannot replace the judgment of an experienced clinician, quantifying the risk of a clinically relevant positive patch test result has the potential to assist in decision making and to inform discussions with patients.

Keywords: contact allergy; logistic regression; machine learning; prediction.