Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method

Background Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide pol...

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Bibliographic Details
Main Authors: Tao Huang, Yuanyuan Li, Simin Wang, Shijie Qiao, Xiujuan Zheng, Wenhui Xiong, Menghan Yang, Xirui Huang, Bizhen Gao
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Annals of Medicine
Subjects:
Metabolic syndrome
machine learning
genetic risk score
prediction model
SHAP
Online Access:https://www.tandfonline.com/doi/10.1080/07853890.2025.2519679
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