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Classification of Obesity among South African Female Adolescents: Comparative Analysis of Logistic Regression and Random Forest Algorithms

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  • Ronel Sewpaul

    (Public Health, Societies and Belonging, Human Sciences Research Council, Merchant House, 2 Dock Rail Road, Cape Town 8001, South Africa)

  • Olushina Olawale Awe

    (Institute of Mathematics, Statistics and Scientific Computing (IMECC), University of Campinas, Campinas 13083-859, Brazil)

  • Dennis Makafui Dogbey

    (Medical Biotechnology and Immunotherapy Research Unit, Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7700, South Africa)

  • Machoene Derrick Sekgala

    (Non-Communicable Diseases, South African Medical Research Council, Cape Town 7505, South Africa)

  • Natisha Dukhi

    (Public Health, Societies and Belonging, Human Sciences Research Council, Merchant House, 2 Dock Rail Road, Cape Town 8001, South Africa)

Abstract

Background: This study evaluates the performance of logistic regression (LR) and random forest (RF) algorithms to model obesity among female adolescents in South Africa. Methods: Data was analysed on 375 females aged 15–17 from the South African National Health and Nutrition Examination Survey 2011/2012. The primary outcome was obesity, defined as body mass index (BMI) ≥ 30 kg/m 2 . A total of 31 explanatory variables were included, ranging from socio-economic, demographic, family history, dietary and health behaviour. RF and LR models were run using imbalanced data as well as after oversampling, undersampling, and hybrid sampling of the data. Results: Using the imbalanced data, the RF model performed better with higher precision, recall, F1 score, and balanced accuracy. Balanced accuracy was highest with the hybrid data (0.618 for RF and 0.668 for LR). Using the hybrid balanced data, the RF model performed better (F1-score = 0.940 for RF vs. 0.798 for LR). Conclusion: The model with the highest overall performance metrics was the RF model both before balancing the data and after applying hybrid balancing. Future work would benefit from using larger datasets on adolescent female obesity to assess the robustness of the models.

Suggested Citation

  • Ronel Sewpaul & Olushina Olawale Awe & Dennis Makafui Dogbey & Machoene Derrick Sekgala & Natisha Dukhi, 2023. "Classification of Obesity among South African Female Adolescents: Comparative Analysis of Logistic Regression and Random Forest Algorithms," IJERPH, MDPI, vol. 21(1), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:21:y:2023:i:1:p:2-:d:1303380
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    References listed on IDEAS

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    1. Shaoyan Zhang & Christos Tjortjis & Xiaojun Zeng & Hong Qiao & Iain Buchan & John Keane, 2009. "Comparing data mining methods with logistic regression in childhood obesity prediction," Information Systems Frontiers, Springer, vol. 11(4), pages 449-460, September.
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