Application and Analysis of Machine Learning in Nutritional Epidemiology
A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutritional Epidemiology".
Deadline for manuscript submissions: 15 July 2025 | Viewed by 44
Special Issue Editor
Special Issue Information
Dear Colleagues,
Nutritional epidemiology explores the relationships between diet, health, and disease within human populations. The emergence of non-communicable diseases (NCDs)—such as heart disease, diabetes, cancer, and chronic respiratory diseases—as leading global causes of mortality underscores the critical need to better understand the long-term impact of diet on health. Traditional methods in nutritional epidemiology face challenges, including the complexity of dietary patterns, measurement errors, and the multifactorial nature of diet-disease relationships.
The application of machine learning (ML) offers innovative opportunities to address these challenges by uncovering complex patterns, improving dietary assessments, and identifying causal pathways. ML methods have the potential to enhance data-driven insights, integrate multidimensional datasets, and provide personalised dietary recommendations for the prevention and management of NCDs.
This Special Issue aims to showcase feature papers (including original research and review articles) on the use of machine learning in nutritional epidemiology. Topics of interest include, but are not limited to, the following:
- Development and application of ML techniques for dietary assessment.
- ML-driven identification of dietary patterns linked to NCDs.
- Integration of machine learning with causal inference methods to study diet–health relationships.
- ML approaches for addressing measurement errors and missing data in dietary studies.
- Predictive modelling of dietary impacts on human and planetary health.
- Ethical and methodological considerations in applying ML to nutritional research.
By advancing the intersection of machine learning and nutritional epidemiology, this Special Issue seeks to foster innovative solutions to reduce diet-related disease burdens, promote sustainable dietary changes, and contribute to the development of healthier and more equitable food systems.
We look forward to your contributions to this important topic.
Dr. Yohannes A. Melaku
Guest Editor
Manuscript Submission Information
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Keywords
- nutritional epidemiology
- non-communicable diseases
- heart disease
- diabetes
- cancer
- chronic respiratory diseases
- diet
- machine learning
- data-driven
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