Extended comparison of machine learning models with internal medicine physicians and clinical risk scores.

van Doorn, William P. T. M.; Stassen, Patricia M.; Borggreve, Hella F.; Schalkwijk, Maaike J.; Stoffers, Judith; Bekers, Otto; Meex, Steven J. R.

doi:10.1371/journal.pone.0245157.s007

pone.0245157.s007.docx (13.12 kB)

Extended comparison of machine learning models with internal medicine physicians and clinical risk scores.

journal contribution

posted on 2021-01-19, 18:27 authored by William P. T. M. van Doorn, Patricia M. Stassen, Hella F. Borggreve, Maaike J. Schalkwijk, Judith Stoffers, Otto Bekers, Steven J. R. Meex

In addition to sensitivity and specificity, we evaluated the performance of each group by positive predictive value (PPV), negative predictive value (NPV), accuracy and area-under-the receiver operating characteristics curve (AUC). Our XGBoost model shows superior performance in each of these metrics, which is in line with the findings presented in the manuscript.

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