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Related Experiment Video

Updated: Jun 28, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study.

Suradech Chaitokkia1, Nitchara Toontom1,2, Tanunchai Boonnuk3

  • 1Program in Health and Safety Technology, Faculty of Public Health, Mahasarakham University, Thailand.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...

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Machine learning models can help stratify cardiovascular risk in elderly Thai adults. The random forest model demonstrated the best performance for predicting heart disease risk in this population.

Area of Science:

  • Gerontology
  • Cardiovascular Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Cardiovascular disease is a leading cause of mortality in aging populations worldwide.
  • Accurate cardiovascular risk stratification is crucial for effective preventive strategies, especially in the elderly.
  • Limited research exists on applying machine learning for cardiovascular risk assessment in the elderly Thai population.

Purpose of the Study:

  • To explore the utility of machine learning models for cardiovascular risk stratification.
  • To identify the best performing machine learning model for this purpose in an elderly Thai cohort.

Main Methods:

  • A cross-sectional study was conducted with 210 hypertensive adults aged 60 years and older.
  • Predictors included age, sex, systolic blood pressure, smoking status, diabetes mellitus, and body mass index.
Keywords:
Cardiovascular risk predictionMachine learningRandom Forest

Related Experiment Videos

Last Updated: Jun 28, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

  • Five-fold cross-validation was used to assess model performance, including accuracy and F1-score.

    • Main Results:

    • The random forest model exhibited the highest overall performance.
    • Achieved accuracy of 67.14 ± 10.47% and an F1-score of 0.57.
    • Indicates potential for ML in identifying at-risk individuals within this demographic.

    Conclusions:

    • Machine learning models show promise for cardiovascular risk stratification in elderly Thai hypertensive patients.
    • The random forest model is a suitable candidate for further development and validation.
    • This approach could enhance early detection and management of cardiovascular disease in older adults.