Prediction of arrhythmia from MIT-BIH database using J48 and k-nearest neighbours (KNN) classifiers

The primary goal of this research is to use J48 and K-Nearest Neighbor (KNN) classifiers to predict arrhythmia using the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed study used the J48 and KNN machine learning algorithms...

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Bibliographic Details
Main Authors: Vinutha, K., Thirunavukkarasu, Usharani
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Arrhythmia
Classifiers
Data mining
K-nearest neighbors algorithm
Machine learning
Statistical analysis
Online Access:Get full text
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Summary:The primary goal of this research is to use J48 and K-Nearest Neighbor (KNN) classifiers to predict arrhythmia using the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed study used the J48 and KNN machine learning algorithms to predict arrhythmia using data from the MIT-BIH dataset consisting of healthy (n=65) and arrhythmia (n=65) ECG signals collected from IEEE dataport in.XLSX format. WEKA 3.8.5, a data mining tool, was used to distinguish between those with arrhythmia and those without. IBM SPSS version 21 was used for the statistical analysis. There was no discernible difference (p=0.025) between the J48 and KNN classifiers. Using WEKA’s 10-fold cross validation to train, test, and verify the classifiers, we find that the J48 classifier is more accurate at classifying data (89.80 percent) than the KNN classifier (87.64 percent).
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0197451