ARCHIVES
Original Article
Multi-Task Deep Learning with SHAP Explainability for Personalized Nutrition Prediction
Johnson Wamucii1
Dr Andrew Kipkebut2
Dr Argan Wekesa3
1Student, Department of Information Technology and Computer Science, The Cooperative University of Kenya, Kenya. 2 3 Lecturer, Department of Information Technology and Computer Science, The Cooperative University of Kenya, Kenya.
Published Online: September-December 2025
Pages: 79-84
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403015
References
1. H. Kassem, A. A. Beevi, S. Basheer, G. Lutfi, L. Cheikh Ismail, and D. Papandreou, “Investigation and assessment of AI’s role in nutrition—An updated narrative review of the evidence,” Nutrients, vol. 17, p. 190, 2025. doi: 10.3390/nu17010190..
2. D. Tsolakidis, L. P. Gymnopoulos, and K. Dimitropoulos, “Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review,” Informatics, vol. 11, no. 3, p. 62, 2024. doi: 10.3390/informatics11030062.
3. Kirk, D., Catal, C., & Tekinerdogan, B. (2021). Precision nutrition: A systematic literature review. Computers in Biology and Medicine, 133, 104365. https://doi.org/10.1016/j.compbiomed.2021.104365.
4. J. Govea, R. Gutierrez, and W. Villegas-Ch, “Transparency and precision in the age of AI: evaluation of explainability-enhanced recommendation systems,” Frontiers in Artificial Intelligence, vol. 7, p. 1410790, 2024. doi: 10.3389/frai.2024.1410790.
5. Montesinos-López, O. A., Chavira-Flores, M., Kismiantini, Crespo-Herrera, L., Saint Pierre, C., Li, H., Fritsche-Neto, R., Al-Nowibet, K., Montesinos-López, A., & Crossa, J., “A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding,” GENETICS, vol. 228, no. 4, iyae161, 2024. doi: 10.1093/genetics/iyae161.
6. S. M. Lundberg and S. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing Systems, vol. 30, no. Nips, pp. 4765–4774, 2017. doi: 10.48550/arXiv.1705.07874.
7. R. Caruana, “Multitask Learning,” Machine Learning, vol. 28, no. 1, pp. 41–75, 1997. doi: 10.1023/A:1007379606734874
8. I. H. Sarker, "Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions," SN Computer Science, vol. 2, no. 4, pp. 420, 2021. doi: 10.1007/s42979-021-00815-1.
9. S. Ruder, “An Overview of Multi-Task Learning in Deep Neural Networks,” arXiv preprint arXiv: 1706.05098, 2017. doi: 10.48550/arXiv.1706.05098.
10. Y. Zhang and Q. Yang, “A Survey on Multi-Task Learning,” arXiv preprint arXiv: 1707.08114, 2017. doi: 10.48550/arXiv.1707.08114.
11. J. Amann, A. Blasimme, E. Vayena, D. Frey, and V. I. Madai, “Explainability for artificial intelligence in healthcare: a multidisciplinary perspective,” BMC Med Inform Decis Mak, vol. 20, no. 310, 2020. doi: 10.1186/s12911-020-01332-6.
2. D. Tsolakidis, L. P. Gymnopoulos, and K. Dimitropoulos, “Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review,” Informatics, vol. 11, no. 3, p. 62, 2024. doi: 10.3390/informatics11030062.
3. Kirk, D., Catal, C., & Tekinerdogan, B. (2021). Precision nutrition: A systematic literature review. Computers in Biology and Medicine, 133, 104365. https://doi.org/10.1016/j.compbiomed.2021.104365.
4. J. Govea, R. Gutierrez, and W. Villegas-Ch, “Transparency and precision in the age of AI: evaluation of explainability-enhanced recommendation systems,” Frontiers in Artificial Intelligence, vol. 7, p. 1410790, 2024. doi: 10.3389/frai.2024.1410790.
5. Montesinos-López, O. A., Chavira-Flores, M., Kismiantini, Crespo-Herrera, L., Saint Pierre, C., Li, H., Fritsche-Neto, R., Al-Nowibet, K., Montesinos-López, A., & Crossa, J., “A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding,” GENETICS, vol. 228, no. 4, iyae161, 2024. doi: 10.1093/genetics/iyae161.
6. S. M. Lundberg and S. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing Systems, vol. 30, no. Nips, pp. 4765–4774, 2017. doi: 10.48550/arXiv.1705.07874.
7. R. Caruana, “Multitask Learning,” Machine Learning, vol. 28, no. 1, pp. 41–75, 1997. doi: 10.1023/A:1007379606734874
8. I. H. Sarker, "Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions," SN Computer Science, vol. 2, no. 4, pp. 420, 2021. doi: 10.1007/s42979-021-00815-1.
9. S. Ruder, “An Overview of Multi-Task Learning in Deep Neural Networks,” arXiv preprint arXiv: 1706.05098, 2017. doi: 10.48550/arXiv.1706.05098.
10. Y. Zhang and Q. Yang, “A Survey on Multi-Task Learning,” arXiv preprint arXiv: 1707.08114, 2017. doi: 10.48550/arXiv.1707.08114.
11. J. Amann, A. Blasimme, E. Vayena, D. Frey, and V. I. Madai, “Explainability for artificial intelligence in healthcare: a multidisciplinary perspective,” BMC Med Inform Decis Mak, vol. 20, no. 310, 2020. doi: 10.1186/s12911-020-01332-6.
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