Predictive Modelling of Health Risks Using Logistic Regression for Personalized Dietary Recommendations
Article Sidebar
Main Article Content
The prevalence of diet-related chronic diseases such as Type 2 Diabetes Mellitus (T2DM) and hypertension necessitates the development of intelligent nutritional systems that go beyond user preference to prioritize clinical safety. Traditional food recommender systems often suffer from a "health-blind" bias, optimizing primarily for taste or popularity. This paper proposes a novel Risk-Aware Diet Recommendation System (RADRS) that integrates Logistic Regression (LR) for interpretable health risk assessment with Linear Programming (LP) for dietary optimization. By training LR models on the NHANES and Pima Indians Diabetes datasets, the system calculates individual disease probabilities. These probabilities dynamically configure nutritional constraints—specifically modifying sodium, carbohydrate, and saturated fat limits—within an LP solver. The proposed architecture bridges the gap between predictive health analytics and personalized meal planning, ensuring that recommendations are both palatable and medically compliant. Keywords—Health Informatics, Logistic Regression, Recommender Systems, Linear Programming, Personalized Nutrition, Chronic Disease Management.
Downloads
References
M. Elsweiler, et al., “Bringing the ‘healthy’ into food recommenders,” CEUR Workshop Proceedings, 2015.
S. Tarima, “Logistic Regression: A Mathematical Approach,” CTSI Biostatistics, 2011.
Y. Chen, et al., “Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph,” in Proc. WSDM ’21, 2021.
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey,” IEEE Transactions on Knowledge and Data Engineering, 2005.
L. Li, et al., “Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning,” IEEE Access, 2023.
A. S. Abdalrada, et al., “Logistic Regression Model for Predicting the Progression of Diabetes,” Iraqi Journal for Computer Science and Mathematics, 2024.
Z. Xie, et al., “Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques,” Preventing Chronic Disease, 2019.
G. Stigler, “The Cost of Subsistence,” Journal of Farm Economics, 1945.
Centers for Disease Control and Prevention (CDC), “National Health and Nutrition Examination Survey Data,” National Center for Health Statistics, 2017–2020.
N. V. Chawla, et al., “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, 2002.
National Cancer Institute (NCI), “Healthy Eating Index–2020,” 2023.
UCI Machine Learning Repository, “Pima Indians Diabetes Database,” 2016.
National Heart, Lung, and Blood Institute (NHLBI), “Framingham Heart Study,” 2023.
D. Dooley, et al., “FoodOn: A harmonized food ontology,” NPJ Science of Food, 2018.
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, 2006.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.