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Original research

Accurate prediction of coronary artery disease across various age groups using machine learning algorithms

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The healthcare sector is generally "information rich". It is required to mine all the information for finding hidden patterns and making wise diagnostic decisions. In order to predict the data in databases and for medical research, particularly in the prediction of heart disease, sophisticated data mining methods are used. In this article, prediction systems for heart failure have been examined using a larger number of input attributes. The method analyses 13 variables, including medical words like sex, blood pressure, and cholesterol, to forecast the risk that a patient will develop heart disease. 13 attributes have so far been used for prediction.In this study, smoking and obesity were introduced as two more characteristics. In the existing we have used Naïve bayes, and Neural networking systems which are having less accuracy compared to the proposed system algorithms. On a database of heart diseases, the proposed data mining classification algorithms J48 Decision Trees, Bagging, and Adaboost are examined. Based on accuracy, these techniques' performances are contrasted. According to our findings, the Adaboost, J48 decision tree and bagging accuracy are 92.1 percent, 91.62 percent, and 90.52 percent, respectively. Our investigation reveals that Adaboost, out of these three classification methods, accurately predicts heart failure.


M.Balamurugan, S. Meera. Accurate Prediction of Coronary Artery Disease Across Various Age Groups Using Machine Learning Algorithms. Cardiometry; Issue 25; December 2022; p.1125-1130; DOI: 10.18137/cardiometry.2022.25.11251130; Available from:


Data mining,  Heart failure,  Adaboost,  J48 Decision Tree,  Bagging and Accuracy
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