Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques.

ABSTRACT:

Background

Bladder cancer (BCa), one of the most common cancers worldwide, is characterized by high rates of recurrence, progression, and mortality. Machine learning algorithms offer promising advancements in enhancing predictive models. This study aims to develop robust machine learning models for predicting BCa survival using clinical and gene expression data.

Methods

Clinical data from BCa patients were obtained from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression models assessed the association between clinical variables and overall survival. Machine learning algorithms, including logistic regression, random forest, XGBoost, decision tree, and LightGBM, were employed to predict survival at 1, 3, and 5 years. The TAGO database

SUBMITTER: Tang Y 

PROVIDER: S-EPMC11546448 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature