Comparative Analysis of Machine Learning Classification Models in Predicting Cardiovascular Disease

Abstract

For a long time, cardiovascular diseases have been the leading cause of death worldwide.
Machine learning has found significant usage in the medical field as it can find patterns in data.
Classification models can help cardiologists to diagnose heart diseases and minimize misdiagnosis
accurately. In this paper, we explored a dataset related to heart disease and compared the accuracy of 43
machine learning classification models. The dataset for this research was downloaded from Kaggle; it
contained 1190 observations, 11 features (age, sex, chest pain type, resting blood pressure, serum
cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise
induced angina, oldpeak, the slope of the peak exercise ST segment) and a binary target variable (no heart
disease or observed cardiovascular disease). For data exploration, preprocessing, training, testing, and
predictor importance analysis, we used MATLAB R2004a software and the Classification Learner app
included in this software. Before training machine learning classification models, we divided the dataset
into a training set (90% of observations) and a test set (10% of observations). To prevent overfitting
during the training of classification models, 10-fold cross-validation was used. The result showed that the
best accuracy was reached with an optimized ensemble classification model (validation accuracy: 0.9262
and test accuracy: 0.9580). After calculating the permutation importance of each feature, we observed that
the most important feature among all 11 features was the slope of the peak exercise ST segment.