Improving nutrient prediction models with polynomial and ratio features and mRMR selection - Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies (Bulletin of Electrical Engineering and Informatics, 2025)
Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.