Machine Learning in Epidemiology
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866908839577649152 |
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| author | Wright, Marvin N. Burk, Lukas Golchian, Pegah Kapar, Jan Koenen, Niklas Langbein, Sophie Hanna |
| author_facet | Wright, Marvin N. Burk, Lukas Golchian, Pegah Kapar, Jan Koenen, Niklas Langbein, Sophie Hanna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16352 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning in Epidemiology Wright, Marvin N. Burk, Lukas Golchian, Pegah Kapar, Jan Koenen, Niklas Langbein, Sophie Hanna Machine Learning Computers and Society 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. |
| title | Machine Learning in Epidemiology |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2602.16352 |