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Machine Learning–Derived Cardiometabolic Risk Burden Identified from Health Checkup Data in a Japanese Population

2026-05-26

Abstract excerpt

<title>Abstract</title> <p>Background Conventional cardiometabolic risk classifications rely on predefined diagnostic thresholds and often fail to capture the continuous, multifactorial, and heterogeneous nature of risk in the general population. In particular, individuals with similar diagnostic profiles may exhibit markedly different underlying risk patterns that are not readily apparent using traditional fram...

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Literature Corpus work
8d2d35af-d3b3-5dea-ae44-98f6dc4c3e1e
DOI
10.21203/rs.3.rs-8571942/v1
Open publication

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Machine Learning–Derived Cardiometabolic Risk Burden Identified from Health Checkup Data in a Japanese PopulationDOI 10.21203/rs.3.rs-8571942/v1
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