This paper is in the following e-collection/theme issue:

Instruments and Questionnaires for Nutrition and Food Intake (166) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (832) Research Instruments, Questionnaires, and Tools (576) Public Health Informatics (93) Behavioural Surveillance for Public Health (229) Obesity and Nutrition as Public Health Problem (304)

Published on in Vol 4, No 2 (2018): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9536, first published .
Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey

Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey

Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey

Authors of this article:

Nicholas Rosso1 Author Orcid Image ;   Philippe Giabbanelli2 Author Orcid Image
Article Authors Cited by (14) Tweetations (2) Metrics

Journals

  1. Bodnar L, Cartus A, Kirkpatrick S, Himes K, Kennedy E, Simhan H, Grobman W, Duffy J, Silver R, Parry S, Naimi A. Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes. The American Journal of Clinical Nutrition 2020;111(6):1235 View
  2. Pillutla V, Tawfik A, Giabbanelli P. Detecting the Depth and Progression of Learning in Massive Open Online Courses by Mining Discussion Data. Technology, Knowledge and Learning 2020;25(4):881 View
  3. Béjar L, García-Perea M, Reyes Ó, Vázquez-Limón E. Relative Validity of a Method Based on a Smartphone App (Electronic 12-Hour Dietary Recall) to Estimate Habitual Dietary Intake in Adults. JMIR mHealth and uHealth 2019;7(4):e11531 View
  4. Oliveira Chaves L, Gomes Domingos A, Louzada Fernandes D, Ribeiro Cerqueira F, Siqueira-Batista R, Bressan J. Applicability of machine learning techniques in food intake assessment: A systematic review. Critical Reviews in Food Science and Nutrition 2023;63(7):902 View
  5. Yang J, Ju X, Liu F, Asan O, Church T, Smith J. Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models. IEEE Open Journal of Engineering in Medicine and Biology 2021;2:291 View
  6. Lutz C, Giabbanelli P. When Do We Need Massive Computations to Perform Detailed COVID‐19 Simulations?. Advanced Theory and Simulations 2022;5(2) View
  7. Côté M, Lamarche B. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism 2022;47(1):1 View
  8. Russo S, Bonassi S. Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients 2022;14(9):1705 View
  9. Djurica D, Kummer T, Mendling J, Figl K. Investigating the impact of representation features on decision model comprehension. European Journal of Information Systems 2023:1 View
  10. Nguyen D, Zigmond S, Glassco S, Tran B, Giabbanelli P. Big data meets storytelling: using machine learning to predict popular fanfiction. Social Network Analysis and Mining 2024;14(1) View
  11. Béjar L. Adherence to the Mediterranean Diet in Association with Self-Perception of Dietary Behavior (Discrepancy between Self-Perceived and Actual Diet Quality): A Cross-Sectional Study among Spanish University Students of Both Genders. Nutrients 2024;16(19):3364 View

Books/Policy Documents

  1. de Lima A, de Sousa Lima R, da Hora H. Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings. View
  2. Sapienza S. Big Data, Algorithms and Food Safety. View