Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17110, first published .
Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Journals

  1. Yu C, Lin Y, Lin C, Lin S, Wu J, Chang S. Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach. Journal of Medical Internet Research 2020;22(6):e18585 View
  2. Lin Y, Chen R, Tang J, Yu C, Wu J, Chen L, Chang S. Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study. JMIR Medical Informatics 2020;8(10):e24305 View
  3. Sheikhtaheri A, Zarkesh M, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Medical Informatics and Decision Making 2021;21(1) View
  4. Yu C, Chang S, Lin C, Lin Y, Wu J, Chen R. Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach. Frontiers in Medicine 2021;8 View
  5. Surodina S, Lam C, Grbich S, Milne-Ives M, van Velthoven M, Meinert E. Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study. JMIRx Med 2021;2(2):e25560 View
  6. Yu C, Chen Y, Chang S, Tang J, Wu J, Lin C. Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach. European Journal of Gastroenterology & Hepatology 2021;33(8):1117 View