GUESS: Projecting Machine Learning Scores to Well-Calibrated Probability Estimates for Clinical Decision Making.

Motivation: Clinical decision-support-systems (CDSS) have been applied in numerous fields, ranging from cancer survival towards drug resistance prediction. Nevertheless, CDSS typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of the current study were 1) to develop a tool for fast and comparative analysis of different calibration methods, 2) to demonstrate their limitations for the use on clinical data, and 3) to introduce our novel method GUESS.

Results: We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles (BBQ), as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research.

Availability: GUESS as part of CalibratR can be downloaded at CRAN.

Supplementary information: Supplementary data are available at Bioinformatics online.