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January 1, 2025Brazilian Journal of Medical and Biological ResearchOpen Access

A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data

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Authors

JAJosé AmorimIBIsabela M. BenseñorAAAirlane Pereira Alencar

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Overview

Cross-sectional analysis identifies key factors in coronary artery calcium scores for individuals with diabetes, suggesting machine learning can enhance risk assessment.

Key Points

  • The best machine learning model achieved an accuracy of 94.8% in predicting positive coronary artery calcium scores.
  • Key variables included age, systolic blood pressure, and body mass index, which are often found in clinical practice.
  • Analysis utilized data from 585 diabetes patients participating in the ELSA-Brasil study, emphasizing diverse sociodemographic factors.
  • Machine learning techniques may improve cardiovascular disease screening by targeting high-risk diabetic patients effectively.

Cite This Study

Amorim et al. (2025) studied this question.

synapsesocial.com/papers/68af76a77567bf4f94feefdbhttps://doi.org/10.1590/1414-431x2025e14986
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