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

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...

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Bibliographic Details
Main Authors: Yali Tang, Shitian Li, Liang Zhu, Lei Yao, Jianlin Li, Xiaoqi Sun, Yuan Liu, Yi Zhang, Xinyang Fu
Format: Article
Language:English
Published: Elsevier 2024-10-01
Series:Heliyon
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Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024142739
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