Classification and predictive models using supervised machine learning: A conceptual review

Abstract

Background: Supervised machine learning models (SMLMs) are likely to be a prevalent approach in the literature on medical machine learning. These models have considerable potential to improve clinical decision-making through enhanced prediction and classification. In this review, we present an overview of SMLMs. We provide a discussion of the conceptual domains relevant to machine learning, model development, validation, and model explanation. This discussion is accompanied by clinical examples to illustrate key concepts. Contribution of the study: This conceptual review provides an overview and guide to the interpretation of SMLMs in the medical literature.

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