  Cardiovascular disease is one of the chronic diseases that is on the rise.
The complications occur when cardiovascular disease is not discovered early and
correctly diagnosed at the right time. Various machine learning approaches,
including ontology-based Machine Learning techniques, have lately played an
essential role in medical science by building an automated system that can
identify heart illness. This paper compares and reviews the most prominent
machine learning algorithms, as well as ontology-based Machine Learning
classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes,
k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine
were among the classification methods explored. The dataset used consists of
70000 instances and can be downloaded from the Kaggle website. The findings are
assessed using performance measures generated from the confusion matrix, such
as F-Measure, Accuracy, Recall, and Precision. The results showed that the
ontology outperformed all the machine learning algorithms.
