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

Journals

  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