Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics

The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

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Publication Details

Journal
BioMedical Engineering OnLine
Published
2024-08-05
DOI
https://doi.org/10.1186/s12938-024-01273-5
Citations
4
Primary Topic
Cardiovascular Disease and Adiposity
Type
article
Field-Weighted Citation Impact
1.61
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article

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics

Zefeng Lu, Hongwei Xu, Linfeng Fu, Cong He et al.
4 citations
BioMedical Engineering OnLine
Cardiovascular Disease and Adiposity
1.61
article

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics

Zefeng Lu, Hongwei Xu, Linfeng Fu, Cong He, Lingting Kong, Fangye Wu, Yingpeng Qi
article en
4 citations

Abstract

The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

BioMedical Engineering OnLineVol. 23(1)
Shaoxing Second Hospital (CN)
Openalex Percentile: Top 15%
Cardiovascular Disease and Adiposity
1.61
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