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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Zeitschriftentitel:
The American Journal of Clinical Nutrition
Jahr:
2024
Nachgewiesen in:
Web of Science
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1016/j.ajcnut.2024.09.003
Verlag / Institution:
Elsevier BV
E-ISSN:
0002-9165
Impact Factor:
6,5
Scimago-Quartil:
Q1
Publikationsdatum:
01.09.2024
Semester:
SS 24
TUM Einrichtung:
Professur für Bewegung, Ernährung und Gesundheit
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