Heart Disease Prediction Using Warm and Naviebayes

Satheesh Kumar A., Vijayalakshmi M.

2025

Abstract

Heart disease cases have been rising quickly lately, thus it's critical to anticipate these conditions. It is challenging to identify the criteria, and because it involves sensitive information, it must be done correctly. We have created an application that estimates an individual's risk of developing heart disease. In the study, which uses a dataset that includes clinical factors like age, sex, kind of chest discomfort, resting blood pressure, cholesterol levels, fasting blood sugar, and others, machine learning algorithms are applied to predict heart disease. We specifically contrasted the effectiveness of Naive Bayes and Decision Tree classifiers. Eighty samples were used for training, and twenty samples were used for testing. Models were trained on the training set and predictions were produced on the testing set. With precision, recall, and F1-score all tightly aligned at 85-88%, the Decision Tree model attained an accuracy of 85%. However, with a 90% score in accuracy, precision, recall, and F1-score, the Naive Bayes model beat the Decision Tree, indicating that it would be more useful in this situation. The models' performance was further examined using confusion matrices, which showed that Naive Bayes also performed better in terms of balancing false positives and false negatives. These results highlight the promise of using machine learning methods to the early identification and detection of cardiac disease.

Paper Citation

in Harvard Style

A. S. and M. V. (2025). Heart Disease Prediction Using Warm and Naviebayes. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 480-485. DOI: 10.5220/0013931700004919

in Bibtex Style

@conference{icrdicct`2525,
author={Satheesh A. and Vijayalakshmi M.},
title={Heart Disease Prediction Using Warm and Naviebayes},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={480-485},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013931700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Heart Disease Prediction Using Warm and Naviebayes
SN - 978-989-758-777-1
AU - A. S.
AU - M. V.
PY - 2025
SP - 480
EP - 485
DO - 10.5220/0013931700004919
PB - SciTePress