PMID: 26776216Jan 19, 2016Paper

INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION

Pacific Symposium on Biocomputing
Ingmar Weber, Palakorn Achananuparp

Abstract

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 difference 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 st...Continue Reading

Related Concepts

Related Feeds

Bioinformatics in Biomedicine

Bioinformatics in biomedicine incorporates computer science, biology, chemistry, medicine, mathematics and statistics. Discover the latest research on bioinformatics in biomedicine here.

Related Papers

Nursing Standard
Grant Byrne
The Proceedings of the Nutrition Society
J V DURNIN
IEEE Journal of Biomedical and Health Informatics
Dinggang ShenBahram Parvin
Nederlands tijdschrift voor geneeskunde
Mirjam Langeveld, J H Hans de Vries
© 2022 Meta ULC. All rights reserved