Machine Learning in Epidemiology

2026-02-18

AI Summary

This study addresses the analytical challenges posed by high-dimensional, complex data in digital epidemiology by proposing an end-to-end methodological framework that integrates interpretable machine learning with epidemiological analysis. The framework systematically combines supervised and unsupervised learning, hyperparameter optimization, and model interpretability techniques, with a fully reproducible implementation in R. Empirical evaluation using a heart disease dataset demonstrates that the proposed approach effectively enhances both model performance and result interpretability. By offering a transparent, reproducible, and generalizable machine learning paradigm tailored to epidemiological research, this work provides a practical foundation for advancing data-driven public health insights while maintaining scientific rigor and interpretability.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Machine Learning
• Epidemiology
• High-dimensional Data
• Digital Epidemiology
• Data Complexity

Innovation

• machine learning
• interpretable machine learning
• hyperparameter optimization
• model evaluation
• digital epidemiology