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

Frailty Detection, Assessment and Prediction (19) Machine Learning (1401) Artificial Intelligence (1216) Decision Support for Health Professionals (1147) Ontologies, Classifications, and Coding (227) Tools, Programs and Algorithms (246) Clinical Information and Decision Making (1340)

Published on in Vol 5, No 4 (2022): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38464, first published .
Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

Authors of this article:

John Oates1 Author Orcid Image ;   Niusha Shafiabady2 Author Orcid Image ;   Rachel Ambagtsheer3 Author Orcid Image ;   Justin Beilby3 Author Orcid Image ;   Chris Seiboth4 Author Orcid Image ;   Elsa Dent3 Author Orcid Image
Article Authors Cited by (4) Tweetations (6) Metrics

John Oates   1 * , PhD ;   Niusha Shafiabady   2 * , BEng (Hon), MSc, PhD ;   Rachel Ambagtsheer   3 , PhD ;   Justin Beilby   3 , MD, PhD ;   Chris Seiboth   4 , BA ;   Elsa Dent   3 , PhD

1 Torrens University, Ultimo, Australia

2 College of Engineering, Information Technology and Environment, Charles Darwin University, Haymarket, NSW, Australia

3 Torrens University, Adelaide, Australia

4 Lutheran Services, Milton, Australia

*these authors contributed equally

Corresponding Author:

  • Niusha Shafiabady, BEng (Hon), MSc, PhD
  • College of Engineering, Information Technology and Environment
  • Charles Darwin University
  • 815 George Street
  • Haymarket, NSW, 2000
  • Australia
  • Phone: 61 80474147
  • Email: niusha.shafiabady@cdu.edu.au