A Hybrid Method: Hierarchical Agglomerative Clustering Algorithm with Classification Techniques for Effective Heart Disease Prediction

Farha Akhter Munmun and Sumi Khatun
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Farha Akhter Munmun: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
Sumi Khatun: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh

International Journal of Research and Innovation in Applied Science, 2022, vol. 7, issue 7, 56-60

Abstract: Prediction of heart disease is challenging because countless data are collected for clinical data analysis, but all this information is not equally important for making the right decisions. We have proposed a hybrid method: Hierarchical Agglomerative Clustering algorithm combined with conventional classification techniques such as K-Nearest Neighbors (K-NN), Decision Tree (J48), and Naïve Bayes which aims to reduce the prediction time by clustering the patients having almost similar symptoms of heart failure. This approach minimizes the forecasting time based on clusters of patients instead of individual patients. Moreover, a comparison between the classification techniques and our approach is depicted based on precision, recall, F1 score, accuracy, and prediction time. The accuracies of the classifiers (K-NN-66.67%, J48-83.33%, and Naïve Bayes83.33%) of our system have slightly decreased compared with the conventional methods (K-NNN-69.128%, J48-83.8926%, and Naïve Bayes-87.248%) but the prediction time was significantly low (K-NNN-230ms, J48-203ms, and Naïve Bayes-195ms).

Date: 2022
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