![[2602.16352] Machine Learning in Epidemiology](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16352] Machine Learning in Epidemiology
Summary
This article explores the application of machine learning in epidemiology, detailing methodologies for data analysis, model evaluation, and interpretability, supported by practical R code examples.
Why It Matters
As the volume and complexity of epidemiological data increase, machine learning offers vital tools for analysis. This article provides foundational knowledge and practical strategies for researchers, enhancing their ability to derive insights from large datasets, which is crucial for public health decision-making.
Key Takeaways
- Machine learning can effectively analyze complex epidemiological data.
- The article covers both supervised and unsupervised learning techniques.
- It emphasizes the importance of model evaluation and hyperparameter optimization.
- Interpretability of machine learning models is crucial for epidemiological applications.
- Practical R code examples facilitate hands-on learning for researchers.
Statistics > Machine Learning arXiv:2602.16352 (stat) [Submitted on 18 Feb 2026] Title:Machine Learning in Epidemiology Authors:Marvin N. Wright, Lukas Burk, Pegah Golchian, Jan Kapar, Niklas Koenen, Sophie Hanna Langbein View a PDF of the paper titled Machine Learning in Epidemiology, by Marvin N. Wright and 5 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (stat.ML); Computers and Society (cs.CY); Machine Learning (cs.LG) Cite as: arXiv:2602.16352 [stat.ML] (or arXiv:2602.16352v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2602.16352 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: In: Ahrens, W., Pigeot, I. (Eds.) Handbook of Epidemiology. Springer, New Y...
