Machine Learning in Epidemiology
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Abstract (arXiv)
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and hyperparameter optimization are developed and interpretable machine learning is introduced. All these theoretical parts are accompanied by code examples in R, where an example dataset on heart disease is used throughout the chapter.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 87 references that are in the map. First posted on arXiv 18 Feb 2026.
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35 of 87 references.
References · 35
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23 more
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