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

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
  4. Alfaro S, Liu J, Naranjo Ortiz C, Alfaro A, Lustberg M. Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review. Supportive Care in Cancer 2026;34(1) View

Conference Proceedings

  1. R S, J V. 2025 4th International Conference on Automation, Computing and Renewable Systems (ICACRS). A LASSO-Regularized Machine Learning Framework for Robust Multiclass Classification of Pediatric Leukemia using Gene Expression Data View