Insights from machine-learned diet success prediction

Ingmar Weber, Palakorn Achananuparp

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider “quantified self“ movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model’s prediction. Our findings include both expected results, such as the token “mcdonalds” or the category “dessert” being indicative for being over the calories goal, but also less obvious ones such as the di erence between pork and poultry concerning dieting success, or the use of the “quick added calories” functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.

Original languageEnglish
Title of host publicationPacific Symposium on Biocomputing 2016, PSB 2016
PublisherWorld Scientific Publishing Co. Pte Ltd
Number of pages12
Publication statusPublished - 2016
Event21st Pacific Symposium on Biocomputing, PSB 2016 - Big Island, United States
Duration: 4 Jan 20168 Jan 2016


Other21st Pacific Symposium on Biocomputing, PSB 2016
CountryUnited States
CityBig Island



  • Calorie counting
  • MyFitnessPal
  • Quantified self
  • Weight loss

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Biomedical Engineering

Cite this

Weber, I., & Achananuparp, P. (2016). Insights from machine-learned diet success prediction. In Pacific Symposium on Biocomputing 2016, PSB 2016 (pp. 540-551). World Scientific Publishing Co. Pte Ltd.