Does Physical Activity Predict Obesity—A Machine Learning and Statistical Method-Based Analysis

Xiaolu Cheng, Shuo-yu Lin, Jin Liu, Shiyong Liu, Jun Zhang, Peng Nie, Bernard F. Fuemmeler, Youfa Wang and Hong Xue
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Xiaolu Cheng: Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA
Shuo-yu Lin: Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA
Jin Liu: Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 23219, USA
Shiyong Liu: Center for Governance Studies, Beijing Normal University at Zhuhai, Zhuhai 519087, China
Jun Zhang: Department of Physics and Engineering, Slippery Rock University of Pennsylvania, Slippery Rock, PA 16057, USA
Bernard F. Fuemmeler: Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond, VA 23219, USA
Youfa Wang: Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710049, China
Hong Xue: Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA

IJERPH, 2021, vol. 18, issue 8, 1-11

Abstract: Background: Obesity prevalence has become one of the most prominent issues in global public health. Physical activity has been recognized as a key player in the obesity epidemic. Objectives: The objectives of this study are to (1) examine the relationship between physical activity and weight status and (2) assess the performance and predictive power of a set of popular machine learning and traditional statistical methods. Methods: National Health and Nutrition Examination Survey (NHANES, 2003 to 2006) data were used. A total of 7162 participants met our inclusion criteria (3682 males and 3480 females), with average age ranging from 48.6 (normal weight) to 52.1 years old (overweight). Eleven classifying algorithms—including logistic regression, naïve Bayes, Radial Basis Function (RBF), local k-nearest neighbors (k-NN), classification via regression (CVR), random subspace, decision table, multiobjective evolutionary fuzzy classifier, random tree, J48, and multilayer perceptron—were implemented and evaluated, and they were compared with traditional logistic regression model estimates. Results: With physical activity and basic demographic status, of all methods analyzed, the random subspace classifier algorithm achieved the highest overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC). The duration of vigorous-intensity activity in one week and the duration of moderate-intensity activity in one week were important attributes. In general, most algorithms showed similar performance. Logistic regression was middle-ranking in terms of overall accuracy, sensitivity, specificity, and AUC among all methods. Conclusions: Physical activity was an important factor in predicting weight status, with gender, age, and race/ethnicity being less but still essential factors associated with weight outcomes. Tailored intervention policies and programs should target the differences rooted in these demographic factors to curb the increase in the prevalence of obesity and reduce disparities among sub-demographic populations.

Keywords: physical activity; obesity; machine learning; disparity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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