Cardiac Disease Prediction using Artificial Intelligence

Machine Learning and big data analytics are providing promising solutions in the fields of healthcare and patient care. These technologies help in the prediction of disease by accurate interpretation of medical data. This early prediction can help in the detection of early signs of diseases, as well...

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Bibliographic Details
Published in:	2023 Advances in Science and Engineering Technology International Conferences (ASET) pp. 1 - 7
Main Authors:	Alhammadi, Ruqya Abdelrahim, Alhosani, Marwa Ali, Al-Ali, Hamda Mahmoud, Soudan, Bassel
Format:	Conference Proceeding
Language:	English
Published:	IEEE 20-02-2023
Subjects:	Artificial Neural Networks Cardiac disease Cardiovascular disease Heart Machine learning Prediction algorithms Smart Medicine Support vector machines Training Training data
Online Access:	Get full text
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Summary:	Machine Learning and big data analytics are providing promising solutions in the fields of healthcare and patient care. These technologies help in the prediction of disease by accurate interpretation of medical data. This early prediction can help in the detection of early signs of diseases, as well as in the planning of proper treatment. Machine learning approaches can be used to predict chronic diseases by developing classification models based on training data. This work outlines a method for predicting cardiac disease, specifically coronary heart disease, using machine learning. The work will examine the prediction accuracy of a number of algorithms such as Naïve Bayes, Decision Trees, Support Vector Machines, and K-Nearest Neighbors. Two training databases are examined: "Framingham Heart Study" and "Cleveland Heart Disease". It will be shown that Decision Tree trained on the Cleveland Heart Disease dataset produced the highest accuracy. One of the advantages of the proposed model is its ability to work on real-time datasets.
ISSN:	2831-6878
DOI:	10.1109/ASET56582.2023.10180650