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Title:

Prediction of individual weight loss using supervised learning: Findings from the CALERIE™ 2 study

Document type:
Zeitschriftenaufsatz
Author(s):
Glasbrenner, Christina; Höchsmann, Christoph; Pieper, Carl F.; Wasserfurth, Paulina; Dorling, James L.; Martin, Corby K.; Redman, Leanne M.; Koehler, Karsten
Abstract:
Background: Predicting individual weight loss responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. Objective: The primary aim of this study was to develop machine learning models to predict individual weight loss responses using only variables known before starting the intervention. In addition, we used machine learning to identify pre-intervention variables influencing the individual weight loss r...     »
Journal title:
The American Journal of Clinical Nutrition
Year:
2024
Covered by:
Web of Science
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1016/j.ajcnut.2024.09.003
Publisher:
Elsevier BV
E-ISSN:
0002-9165
Impact Factor:
6,5
Scimago Quartil:
Q1
Date of publication:
01.09.2024
Semester:
SS 24
TUM Institution:
Professur für Bewegung, Ernährung und Gesundheit
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