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Optimizing diverse machine learning models to identify an optimal predictor for accurately forecasting the 10-year risk of diagnosing Coronary Artery Disease. Leveraging a range of health indicators and predictors, this project aims to enhance prediction accuracy and contribute valuable insights into proactive healthcare.

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Computational Diagnosis of Coronary Heart Disease

Project Overview

In this project, I provide a demonstration of logistic regression to use for the computational diagnosis of coronary heart disease, and walk through the entire model development process including validating assumptions, feature selection, and monitoring accuracy. Further testing of machine learning models is also done as additional comparisons for propspective quantitative analysis.

Built With

This project was built with the following technologies:

  • R
  • Tidyverse
  • dplyr
  • ggplot
  • MASS
  • faraway
  • pROC
  • caret

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Optimizing diverse machine learning models to identify an optimal predictor for accurately forecasting the 10-year risk of diagnosing Coronary Artery Disease. Leveraging a range of health indicators and predictors, this project aims to enhance prediction accuracy and contribute valuable insights into proactive healthcare.

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