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

Theme Issue 2024: Artificial Intelligence in Oncology (3) Clinical Information and Decision Making (1606) Artificial Intelligence (1670) Data Science (300) Precision Medicine (95) Machine Learning (1691) Cancer Survivorship (281) Cancer Epidemiology, Cancer Surveillance and Infodemiology (90) mHealth for Wellness, Behavior Change and Prevention (3072)

Published on in Vol 6 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65001, first published .
A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study

A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study

A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study

Authors of this article:

Tracy Huang1 Author Orcid Image ;   Chun-Kit Ngan2 Author Orcid Image ;   Yin Ting Cheung3 Author Orcid Image ;   Madelyn Marcotte2 Author Orcid Image ;   Benjamin Cabrera4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Tracy Huang   1 * , BA ;   Chun-Kit Ngan   2 * , BA, PhD ;   Yin Ting Cheung   3 , PhD ;   Madelyn Marcotte   2 , BSc ;   Benjamin Cabrera   4 , BSc

1 Emory University, Atlanta, GA, United States

2 Worcester Polytechnic Institute, Worcester, MA, United States

3 Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

4 Arizona State University, Tempe, AZ, United States

*these authors contributed equally

Corresponding Author:

  • Chun-Kit Ngan, BA, PhD
  • Worcester Polytechnic Institute
  • 100 Institute Rd
  • Worcester, MA, 01609
  • United States
  • Phone: 1 (508) 831 5000
  • Email: cngan@wpi.edu