Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial

TL;DR

This tutorial provides practical guidance on evaluating machine learning algorithms for healthcare applications, emphasizing reliable performance assessment, common pitfalls, and understanding evaluation criteria to improve real-world deployment.

Contribution

It offers a comprehensive overview of performance evaluation methods and best practices specifically tailored for machine learning in healthcare, addressing common misconceptions and challenges.

Findings

Highlights key performance metrics for healthcare ML

Identifies common evaluation pitfalls and how to avoid them

Provides practical guidelines for reliable performance assessment

Abstract

Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently. This development is thanks to the increase in data availability as well as advances in artificial intelligence and machine learning research. Highly promising research examples are published daily. However, at the same time, there are some unrealistic expectations with regards to the requirements for reliable development and objective validation that is needed in healthcare settings. These expectations may lead to unmet schedules and disappointments (or non-uptake) at the end-user side. It is the aim of this tutorial to provide practical guidance on how to assess performance reliably and efficiently and avoid common traps. Instead of giving a list of do's and don't s, this tutorial tries to build a better understanding behind these do's and don't s and presents both the most relevant performance evaluation criteria as well as how to compute them. Along the way, we will indicate common mistakes and provide references discussing various topics more in-depth.