This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2832) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (917) Machine Learning (1743) Demographics of Users, Social & Digital Divide (836) Obesity and Nutrition as Public Health Problem (349)

Published on in Vol 6, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40404, first published .
Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study

Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study

Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study

Authors of this article:

Jyh Eiin Wong1 Author Orcid Image ;   Miwa Yamaguchi2 Author Orcid Image ;   Nobuo Nishi2 Author Orcid Image ;   Michihiro Araki2 Author Orcid Image ;   Lei Hum Wee1, 3 Author Orcid Image
Article Authors Cited by (12) Tweetations (5) Metrics

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  1. Kebede N, Mohammed Y, Kasaye M, Zewdie A. Application of Health Belief Model to Assess Predictors of Weight Management Behaviour Intention Among Civil Servants in Ethiopia: A Mixed Method Study. Diabetes, Metabolic Syndrome and Obesity 2023;Volume 16:3339 View
  2. Annapoorna E, Nithin Sai P, Raj Shreyas Goud K, Koushik K, Saini M, Swadesh Kumar S. Automated Diet and Exercise Suggestion based on Obesity Classification. E3S Web of Conferences 2023;430:01049 View
  3. Delpino F, Costa Â, César do Nascimento M, Dias Moura H, Geremias dos Santos H, Wichmann R, Porto Chiavegatto Filho A, Arcêncio R, Nunes B. Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis. Nutrition, Metabolism and Cardiovascular Diseases 2024;34(9):2034 View
  4. Yadav V, Mohan S, Agarwal S, de Godoy L, Rajan A, Nasrallah M, Bagley S, Brem S, Loevner L, Poptani H, Singh A, Chawla S. Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O6-methylguanine-methyltransferase promoter methylation status. Neuro-Oncology Advances 2024;6(1) View
  5. Jawara D, Lauer K, Venkatesh M, Stalter L, Hanlon B, Churpek M, Funk L. Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program. Journal of Surgical Research 2025;306:43 View
  6. Zeng Y, Lu H, Li S, Shi Q, Liu L, Gong Y, Yan P. Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model. Drug Design, Development and Therapy 2025;Volume 19:239 View
  7. Niakan Kalhori S, Najafi F, Hasannejadasl H, Heydari S. Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis. International Journal of Medical Informatics 2025;196:105804 View
  8. Değirmenci A. Machine Learning Models for Accurate Prediction of Obesity: A Data-Driven Approach. Turkish Journal of Science and Technology 2025;20(1):77 View
  9. Boakye N, O'Toole C, Jalali A, Hannigan A. Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis. International Journal of Medical Informatics 2025;199:105887 View

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Conference Proceedings

  1. Kadam S, Narwade D, Patil S, Mukkavar S, Patil V, Futane P. 2024 IEEE 9th International Conference for Convergence in Technology (I2CT). Obesity Detection in Adults using Machine Learning View
  2. Nikhita G, Varma P, Teja S, Rao G. 2024 9th International Conference on Communication and Electronics Systems (ICCES). Obesity level prediction using ML based on eating habits and physical condition View