Improving nutrient prediction models with polynomial and ratio features and mRMR selection - Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies (Institute of Advanced Engineering and Science, 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 (