Predicting Overweight and Obesity in Later Life from Childhood Data : A Review of Predictive Modeling Approaches
Publiceringsår
2022
Upphovspersoner
Rautiainen, Ilkka; Äyrämö, Sami
Abstrakt
Overweight and obesity are an increasing phenomenon worldwide. Reliable and accurate prediction of future overweight or obesity early in the childhood could enable effective interventions by experts. While a lot of research has been done using explanatory modeling methods, capability of machine learning, and predictive modeling, in particular, remain mainly unexplored. In predictive modeling, the models are validated with previously unseen examples, giving a more accurate estimate of their performance and generalization ability in real-life scenarios. Our objective was to find and review existing overweight or obesity research from the perspectives of childhood data and predictive modeling. Thirteen research articles and three review articles were identified as relevant for this review. In general, prediction models with high performance either have a short time span to predict and/or are based on late childhood data. Logistic regression is currently the most often used method in forming the prediction models, although recently more complex models have also been applied. In addition to child’s own weight and height information, maternal weight status and body mass index were often used as predictors in the models. More recent research has started to focus on a wider variety of other predictors as well.
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Publikationstyp
Publikationsform
Artikel
Moderpublikationens typ
Samlingsverk
Artikelstyp
Annan artikel
Målgrupp
VetenskapligKollegialt utvärderad
Kollegialt utvärderadUKM:s publikationstyp
A3 Del av bok eller annat samlingsverkPublikationskanalens uppgifter
Moderpublikationens namn
Moderpublikationens redaktörer
Tuovinen, Tero T.; Periaux, Jacques; Neittaanmäki, Pekka
Förläggare
Sidor
203-220
ISSN
ISBN
Publikationsforum
Publikationsforumsnivå
2
Öppen tillgång
Öppen tillgänglighet i förläggarens tjänst
Nej
Parallellsparad
Nej
Övriga uppgifter
Vetenskapsområden
Data- och informationsvetenskap; Folkhälsovetenskap, miljö och arbetshälsa
Nyckelord
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Publiceringsland
Schweiz
Förlagets internationalitet
Internationell
Språk
engelska
Internationell sampublikation
Nej
Sampublikation med ett företag
Nej
DOI
10.1007/978-3-030-70787-3_14
Publikationen ingår i undervisnings- och kulturministeriets datainsamling
Ja