Machine learning-based prediction of LDL cholesterol: performance evaluation and validation

Machine learning models offer more accurate LDL-C estimates, especially in high TG contexts where traditional formulas are less reliable. These models could enhance cardiovascular risk assessment by providing more precise LDL-C estimates, potentially leading to more informed treatment decisions and improved patient outcomes.

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Publication Details

Journal
PeerJ
Published
2025-04-09
DOI
https://doi.org/10.7717/peerj.19248
Citations
2
Primary Topic
Lipoproteins and Cardiovascular Health
Type
article
Field-Weighted Citation Impact
4.01
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article

Machine learning-based prediction of LDL cholesterol: performance evaluation and validation

C. Jiang, Jun Meng, Zhongming An
2 citations
PeerJ
Lipoproteins and Cardiovascular Health
4.01
article

Machine learning-based prediction of LDL cholesterol: performance evaluation and validation

C. Jiang, Jun Meng, Zhongming An
article en
2 citations

Abstract

Machine learning models offer more accurate LDL-C estimates, especially in high TG contexts where traditional formulas are less reliable. These models could enhance cardiovascular risk assessment by providing more precise LDL-C estimates, potentially leading to more informed treatment decisions and improved patient outcomes.

PeerJVol. 13
Yanbian University Hospital (CN)
Openalex Percentile: Top 8%
Lipoproteins and Cardiovascular Health
4.01
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Machine learning-based prediction of LDL cholesterol: performance evaluation and validation — C. Jiang, Jun Meng, et al. · PeerJ (2025) | TGRS Research Map | TGRS