Explainable Artificial Intelligence for Predicting Calorie Imbalance

DOI

10.2991/978-94-6463-998-8_5How to use a DOI?

Keywords

Calorie Imbalance; Machine Learning; SHAP; Lifestyle Factors; Precision Nutrition

Abstract

This study demonstrates the potential of machine learning in predicting calorie imbalance by integrating physiological, behavioral, and dietary features into an interpretable analytical framework. Using Logistic Regression and Random Forest models, the results showed that both approaches achieved high predictive accuracy, with Logistic Regression slightly outperforming Random Forest in overall classification metrics. The findings indicate that even relatively simple and transparent models can capture the complex interplay between energy intake and expenditure when supported by well-prepared and meaningful data. The SHAP analysis provided valuable insights into the relative importance of individual variables, highlighting that calorie intake, calories burned, work-out type, and session duration were the most influential determinants of energy balance. These factors emphasize the pivotal role of daily activity patterns and dietary habits in shaping caloric equilibrium. Meanwhile, demographic attributes such as age and gender contributed less significantly, suggesting that lifestyle behavior exerts a stronger and more direct effect on calorie imbalance. For future research, it is recommended to expand the dataset with more diverse demographic and geographic samples to improve generalisability across populations. Incorporating longitudinal data would also allow for temporal tracking of energy balance dynamics rather than single-point estimation. Furthermore, integrating additional behavioural and psychological variables—such as stress level, motivation, or eating context—may enrich model interpretability and predictive depth. Finally, future studies could explore the deployment of these interpretable models in mobile health platforms, enabling real-time monitoring and personalised feedback for users aiming to maintain energy balance in daily life.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Syarifah Atika
AU  - Andrian Reinaldo Crispin
AU  - Abdi Dharma
AU  - Yennimar Yennimar
AU  - Brian Gomez Ricafort
AU  - Pratik Bibhisan Kamble
PY  - 2026
DA  - 2026/03/05
TI  - Explainable Artificial Intelligence for Predicting Calorie Imbalance
BT  - Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)
PB  - Atlantis Press
SP  - 32
EP  - 39
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-998-8_5
DO  - 10.2991/978-94-6463-998-8_5
ID  - Atika2026
ER  -