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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Natriuretic Peptides (BNP)
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Related Experiment Video

Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and

Bahareh Behkamal1,2, Fatemeh Asgharian Rezae2, Amin Mansoori3

  • 1Medicinal Chemistry Department, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, 9177899191, Iran.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict Metabolic Syndrome (MetS) using liver function tests and hs-CRP. Gradient Boosting and CNNs showed high specificity, enabling early, non-invasive MetS detection.

Keywords:
High-sensitivity C-reactive protein (hs-CRP)Liver function testsMachine learningMetabolic syndrome

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Area of Science:

  • Biomedical Informatics
  • Cardiovascular Disease Research
  • Clinical Diagnostics

Background:

  • Metabolic Syndrome (MetS) is a cluster of conditions increasing risks for heart disease, stroke, and diabetes.
  • Early detection and intervention are vital for mitigating severe health consequences.
  • Current diagnostic methods may lack efficiency for widespread screening.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) framework for predicting Metabolic Syndrome (MetS).
  • To utilize serum liver function tests and hs-CRP as predictive biomarkers.
  • To assess the performance of various ML algorithms in MetS identification.

Main Methods:

  • Implemented an ML predictive framework using Alanine Transaminase (ALT), Aspartate Aminotransferase (AST), Direct Bilirubin (BIL.D), Total Bilirubin (BIL.T), and hs-CRP.
  • Integrated and compared Linear Regression, Decision Trees, SVM, Random Forest, Balanced Bagging, Gradient Boosting (GB), and Convolutional Neural Networks (CNNs).
  • Validated the framework on a large cohort (8,972 participants) from the MASHAD study (2010-2020).

Main Results:

  • Gradient Boosting (GB) and Convolutional Neural Networks (CNNs) exhibited superior performance.
  • CNN achieved a specificity of 83%, while GB achieved 77% specificity.
  • The GB model demonstrated the lowest error rate (27%), indicating strong predictive power.
  • SHAP analysis highlighted hs-CRP, BIL.D, ALT, and sex as key predictors.

Conclusions:

  • An automated ML pipeline using liver function biomarkers and hs-CRP can facilitate early, non-invasive MetS detection.
  • This approach supports clinical decision-making and risk stratification for MetS.
  • The findings underscore the potential of ML in enhancing cardiovascular and metabolic disease screening.