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Predicting Coronary Heart Disease Using a Suite of Machine Learning Models

Jamal Al-Karaki, Philip Ilono, Sanchit Baweja, Jalal Naghiyev, Raja Singh Yadav, Muhammad Al-Zafar Khan

TL;DR

Supervised learning via machine learning algorithms presents a low-cost (computationally speaking), non-invasive solution that can be a precursor for early diagnosis of Coronary Heart Disease.

Abstract

Coronary Heart Disease affects millions of people worldwide and is a well-studied area of healthcare. There are many viable and accurate methods for the diagnosis and prediction of heart disease, but they have limiting points such as invasiveness, late detection, or cost. Supervised learning via machine learning algorithms presents a low-cost (computationally speaking), non-invasive solution that can be a precursor for early diagnosis. In this study, we applied several well-known methods and benchmarked their performance against each other. It was found that Random Forest with oversampling of the predictor variable produced the highest accuracy of 84%.

Predicting Coronary Heart Disease Using a Suite of Machine Learning Models

TL;DR

Supervised learning via machine learning algorithms presents a low-cost (computationally speaking), non-invasive solution that can be a precursor for early diagnosis of Coronary Heart Disease.

Abstract

Coronary Heart Disease affects millions of people worldwide and is a well-studied area of healthcare. There are many viable and accurate methods for the diagnosis and prediction of heart disease, but they have limiting points such as invasiveness, late detection, or cost. Supervised learning via machine learning algorithms presents a low-cost (computationally speaking), non-invasive solution that can be a precursor for early diagnosis. In this study, we applied several well-known methods and benchmarked their performance against each other. It was found that Random Forest with oversampling of the predictor variable produced the highest accuracy of 84%.
Paper Structure (13 sections, 9 equations, 3 figures, 2 tables)

This paper contains 13 sections, 9 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Theory
  4. Logistic Regression
  5. Linear Discriminant Analysis
  6. Support Vector Machines
  7. Decision Trees
  8. Random Forest
  9. Naive Bayes
  10. $K$-Nearest Neighbors
  11. Extreme Gradient Boosting
  12. Experiments
  13. Conclusion

Figures (3)

  • Figure 1: Counts of the classes in the TenYearCHD variable
  • Figure 2: Missing values per feature. Only those features that have missing values are depicted.
  • Figure 3: Left: ROC. Right: Precision-Recall curve for the random forest model with oversampling.