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

Theme Issue 2025: Artificial Intelligence in Oncology (5) Clinical Information and Decision Making (1757) Artificial Intelligence (1914) Data Science (333) Precision Medicine (126) Machine Learning (1884) Cancer Survivorship (304) Cancer Epidemiology, Cancer Surveillance and Infodemiology (99) mHealth for Wellness, Behavior Change and Prevention (3187)

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 (3) Tweetations Metrics

Journals

  1. Reddy K, Mishra D. Advances in Feature Selection Using Memetic Algorithms: A Comprehensive Review. WIREs Data Mining and Knowledge Discovery 2025;15(2) View
  2. Badilla-Salamanca M, Medina Durán R, Contreras-Bolton C. An effective multi-objective metaheuristic for the support vector machine with feature selection. Knowledge-Based Systems 2025;328:114203 View
  3. Reunamo A, Moen H, Salanterä S, Lähteenmäki P. Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support. Frontiers in Digital Health 2025;7 View