A review of machine learning in obesity

Summary Rich sources of obesity‐related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to de...

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Bibliographic Details
Published in:	Obesity reviews Vol. 19; no. 5; pp. 668 - 685
Main Authors:	DeGregory, K. W., Kuiper, P., DeSilvio, T., Pleuss, J. D., Miller, R., Roginski, J. W., Fisher, C. B., Harness, D., Viswanath, S., Heymsfield, S. B., Dungan, I., Thomas, D. M.
Format:	Journal Article
Language:	English
Published:	England Wiley Subscription Services, Inc 01-05-2018
Subjects:	Algorithms Artificial intelligence Artificial neural networks Classification Cluster analysis Data analysis Data processing Decision analysis Deep learning Humans Learning algorithms Logistic Models Machine learning Machine Learning - statistics & numerical data Medical electronics National Health and Nutrition Examination Survey Neural networks Neural Networks, Computer Nutrition Nutrition Surveys Obesity Principal components analysis Regression analysis Reviews Smartphones topological data analysis Deep learning National Health and Nutrition Examination Survey machine learning topological data analysis
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Summary:	Summary Rich sources of obesity‐related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity‐related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high‐level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-2
ISSN:	1467-7881 1467-789X
DOI:	10.1111/obr.12667