A machine-learned model for predicting weight loss success using weight change features early in treatment
Farzad Shahabi, Samuel L. Battalio, Angela Fidler Pfammatter, Donald Hedeker, Bonnie Spring, Nabil Alshurafa · 2024
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Description
Farzad Shahabi and co-authors build a learning model that predicts who will succeed at weight loss, using only early changes in weight, in a paper from 2024. Care programs that step up help for slow starters can save effort, but they need a sound rule for spotting those people early.
The team trained a random forest model on data from a past trial with a stepped design. It learned from weight change in the first two weeks, along with the range and slope of that change and age. The model sorted success at six months with strong accuracy. It was then tested on two other studies with new groups of adults with obesity. Scores held up well across all three studies, with no large drop on the new data.
Catalog record
| CREATOR | Farzad Shahabi, Samuel L. Battalio, Angela Fidler Pfammatter, Donald Hedeker, Bonnie Spring, Nabil Alshurafa |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |