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

Author

Listed:
  • 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)

  • Peng Nie

    (Department of Economics, School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710049, China)

  • 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)

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.

Suggested Citation

  • Xiaolu Cheng & Shuo-yu Lin & Jin Liu & Shiyong Liu & Jun Zhang & Peng Nie & Bernard F. Fuemmeler & Youfa Wang & Hong Xue, 2021. "Does Physical Activity Predict Obesity—A Machine Learning and Statistical Method-Based Analysis," IJERPH, MDPI, vol. 18(8), pages 1-11, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:3966-:d:533104
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    References listed on IDEAS

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    3. Goodarz Danaei & Eric L Ding & Dariush Mozaffarian & Ben Taylor & Jürgen Rehm & Christopher J L Murray & Majid Ezzati, 2009. "The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors," PLOS Medicine, Public Library of Science, vol. 6(4), pages 1-23, April.
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    Cited by:

    1. Dana Badau & Adela Badau & Cristian Trambitas & Dia Trambitas-Miron & Raluca Moraru & Alexandru Antoniu Stan & Bogdan Marian Oancea & Ioan Turcu & Emilia Florina Grosu & Vlad Teodor Grosu & Lucia Geor, 2021. "Differences between Active and Semi-Active Students Regarding the Parameters of Body Composition Using Bioimpedance and Magnetic Bioresonance Technologies," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    2. Elżbieta Biernat & Monika Piątkowska & Michał Rozpara, 2022. "Is the Prevalence of Low Physical Activity among Teachers Associated with Depression, Anxiety, and Stress?," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

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