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Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods.
Hu G, Ahmed M, L'Abbé MR. Hu G, et al. Am J Clin Nutr. 2023 Mar;117(3):553-563. doi: 10.1016/j.ajcnut.2022.11.022. Epub 2022 Dec 23. Am J Clin Nutr. 2023. PMID: 36872019
For FSANZ score prediction, our proposed method reached a similar prediction accuracy (R(2): 0.87 and MSE: 14.4) compared with bag-of-words methods (R(2): 0.72-0.84; MSE: 30.3-17.6), whereas structured nutrition facts machine learning model performed the best (R …
For FSANZ score prediction, our proposed method reached a similar prediction accuracy (R(2): 0.87 and MSE: 14.4) compared with bag-of …