<![CDATA[The rapid escalation of global metabolic health concerns emphasizes the critical urgency for advanced technological solutions that facilitate precise and automated monitoring of daily caloric intake. This research conducts a rigorous comparative analysis to evaluate the predictive performance and computational efficiency of Random Forest (RF) and Support Vector Machine (SVM) algorithms in classifying food calorie levels. The methodology commenced with a comprehensive data preprocessing phase involving multi-strategy missing value imputation and the discretization of caloric values into ordinal categories. Feature selection was meticulously executed using linear regression coefficients to identify high-impact nutritional variables. To ensure a robust evaluation, the dataset was partitioned using an 80:20 ratio for training and testing, complemented by cross-validation to minimize bias and variance. Experimental results indicated that the Random Forest (RF) demonstrated superior classification capabilities, achieving a peak accuracy of 94.8% alongside balanced precision and recall scores. Statistical evaluation via confusion matrices further revealed that Random Forest exhibited enhanced generalization across high-dimensional nutritional features compared to the geometric approach of Support Vector Machine (SVM). Furthermore, the analysis of computational overhead provided critical insights into the real-time deployment feasibility of each model. Ultimately, the findings suggest that the Random Forest serves as a robust engine for personalized dietary management systems, offering a reliable framework for future developments in preventive digital healthcare. By successfully bridging machine learning with nutritional science, this study establishes a benchmark for high-accuracy food classification essential for modern health-centric mobile applications.]]>
