A3 Book section, Chapters in research books
Predicting Overweight and Obesity in Later Life from Childhood Data : A Review of Predictive Modeling Approaches (2022)

Rautiainen, I., & Äyrämö, S. (2022). Predicting Overweight and Obesity in Later Life from Childhood Data : A Review of Predictive Modeling Approaches. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 203-220). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_14

JYU authors or editors

Publication details

All authors or editors: Rautiainen, Ilkka; Äyrämö, Sami

Parent publication: Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges

Parent publication editors: Tuovinen, Tero T.; Periaux, Jacques; Neittaanmäki, Pekka

ISBN: 978-3-030-70786-6

eISBN: 978-3-030-70787-3

Journal or series: Intelligent Systems, Control and Automation: Science and Engineering

ISSN: 2213-8986

eISSN: 2213-8994

Publication year: 2022

Number in series: 76

Pages range: 203-220

Number of pages in the book: 275

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-70787-3_14

Publication open access: Not open

Publication channel open access: 

Web address of parallel published publication (pre-print): https://arxiv.org/abs/1911.08361

Additional information: The CSAI 2019 Conference (Computational Science and AI in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges) took place at Jyväskylä, Finland, on June 12–14, 2019.

Abstract

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.

Keywords: overweight; obesity; childhood; adulthood; forecasts; modelling (representation); machine learning; artificial intelligence; literature surveys

Free keywords: predictive models; machine learning; Artificial intelligence; obesity; overweight

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 2