Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.

ABSTRACT:

Background

Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to screen participants with high risk of PCa and, specifically, investigated whether participants with metabolic syndrome (MetS) exhibited an elevated PCa risk.

Methods

A prospective cohort study was performed with 41,837 participants in South Korea. We predicted PCa based on MetS, its components, and sociodemographic factors using Cox proportional hazards and five ML models. Integrated Brier score (IBS) and C-index were used to assess model performance.

Results

A total of 210 incident PCa cases were identified. We found good calib

SUBMITTER: Tran TT 

PROVIDER: S-EPMC11662620 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature