@article{
 title = {A review of machine learning in obesity},
 type = {article},
 year = {2018},
 keywords = {Deep learning,National Health and Nutrition Examination Survey,machine learning,topological data analysis},
 volume = {19},
 id = {dc4bf489-fc6e-32a1-8ff9-11bbe7cbd6c3},
 created = {2023-10-25T08:56:39.833Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:39.833Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {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.},
 bibtype = {article},
 author = {DeGregory, K.W. and Kuiper, P. and DeSilvio, T. and Pleuss, J.D. and Miller, R. and Roginski, J.W. and Fisher, C.B. and Harness, D. and Viswanath, S. and Heymsfield, S.B. and Dungan, I. and Thomas, D.M.},
 doi = {10.1111/obr.12667},
 journal = {Obesity Reviews},
 number = {5}
}

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