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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 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.
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 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.
Cardiac Disease Prediction using Artificial Intelligence
Alhammadi, Ruqya Abdelrahim (author) / Alhosani, Marwa Ali (author) / Al-Ali, Hamda Mahmoud (author) / Soudan, Bassel (author)
2023-02-20
707587 byte
Conference paper
Electronic Resource
English
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