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

ABSTRACT:

Background

The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments.

Objective

This study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer.

Methods

We devised a hybrid deep learning-based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. Within a data-driven, clinical domain-guided framework to select the best set of features

SUBMITTER: Huang T 

PROVIDER: S-EPMC11950700 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature