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

Research Methodology - AI (14) Innovative Methods in Public Health and Surveillance (344) Machine Learning (1535) Diabetes (198) Diabetes Health Services and System Innovations (120) Diabetes Surveillance and Epidemiology (27) Diabetes-specific EHR Improvements (33) Tools, Programs and Algorithms (280) Clinical Informatics (1065)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58463, first published .
Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation

Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation

Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation

Authors of this article:

Joshua Kuan Tan1 Author Orcid Image ;   Le Quan2 Author Orcid Image ;   Nur Nasyitah Mohamed Salim1 Author Orcid Image ;   Jen Hong Tan2 Author Orcid Image ;   Su-Yen Goh3 Author Orcid Image ;   Julian Thumboo1 Author Orcid Image ;   Yong Mong Bee3 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Husain G, Nasef D, Jose R, Mayer J, Bekbolatova M, Devine T, Toma M. SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models. Algorithms 2025;18(1):37 View