The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms

TL;DR

This paper evaluates the effectiveness of ontology-based machine learning techniques in predicting cardiovascular disease, demonstrating that ontology integration improves classification performance over traditional algorithms.

Contribution

It introduces a comparative analysis showing that ontology-enhanced machine learning outperforms standard algorithms in cardiovascular disease prediction.

Findings

Ontology-based methods outperform traditional machine learning algorithms.

Support Vector Machine achieved the highest accuracy among tested algorithms.

Ontology integration improves diagnostic prediction performance.

Abstract

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.