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Related Concept Videos

Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Updated: May 22, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a

Thien Vu1,2,3, Yoshihiro Kokubo4, Mai Inoue1

  • 1Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Osaka, 566-0002, Japan, 81 8093069457.

JMIR Cardio
|May 12, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts coronary heart disease (CHD) risk using novel factors like calcium levels and body fat. This approach aids healthcare professionals in identifying multifactorial risks for effective prevention strategies.

Keywords:
CHDExtreme Gradient BoostingLight Gradient-Boosting MachineLightGBMSHAPSVMShapley Additive ExplanationsXGBoostcoronary heart diseaselogistic regressionmachine learningrandom forestsupport vector machine

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Area of Science:

  • Cardiovascular Disease Research
  • Machine Learning in Healthcare
  • Biostatistics and Epidemiology

Background:

  • Coronary heart disease (CHD) is a leading global cause of death, necessitating improved risk assessment and prevention strategies.
  • Traditional models for CHD risk assessment have limitations due to their reliance on predefined variables.
  • Machine learning (ML) offers advanced analytical capabilities for complex, high-dimensional data to uncover novel CHD predictors.

Purpose of the Study:

  • To evaluate the predictive power of established and novel risk factors for CHD incidence using ML techniques.
  • To compare the performance of various ML models in predicting CHD.
  • To identify key predictors of CHD and understand their contribution using model interpretation methods.

Main Methods:

  • A cohort of 7260 participants (aged 30-84) from Suita City, Japan, was followed for an average of 15 years for cardiovascular events.
  • Five ML models (logistic regression, random forest, SVM, XGBoost, LightGBM) were employed to predict CHD incidence.
  • Model performance was assessed using metrics like accuracy, AUC, and calibration, with Shapley Additive Explanations (SHAPs) used for risk factor interpretation.

Main Results:

  • The Random Forest model achieved the highest predictive performance (accuracy 0.73, AUC 0.73).
  • Key predictors included intima-media thickness, blood pressure, lipid profiles, and estimated glomerular filtration rate.
  • Novel significant contributors to CHD risk were identified as lower calcium levels, elevated white blood cell counts, and body fat percentage, with a protective effect noted in women.

Conclusions:

  • A robust ML model, interpreted by SHAP, effectively predicts CHD incidence by considering multifactorial risks.
  • The findings underscore the importance of integrating novel biomarkers and considering gender-specific factors in CHD risk assessment.
  • This data-driven approach can support healthcare professionals in developing targeted prevention strategies for cardiovascular health.