Key result
The Support Vector Machine (SVM) model achieved the highest accuracy of 91.41% in predicting cardiac arrhythmia using an 80/20 train-test data split.
Why the study?
Arrhythmia can lead to sudden death, blood clots, heart failure, or stroke without proper precautions, prompting the adoption of machine learning algorithms to foresee cardiac arrhythmia.
A Support Vector Machine (SVM) model achieved 91.41% accuracy in predicting cardiac arrhythmia from ECG data, outperforming Logistic Regression and Decision Tree algorithms.
SVM model may aid arrhythmia prediction; leaves open prospective validation before clinical use.
The Heart is one of the most important organ responsible for sustaining Human life. The Normal functioning of it is very important but the irregular functioning of it will causes few problems which may be classified as different heart disease. Arrhythmia an Irregular Heart Beat, which is considered as one of the Cardio Vascular Disease. Electrocardiogram (ECG) is the most preferred tool used to capture Heart Beat. Without taking proper pre-cautionary measures this may lead to sudden death, blood clots, heart failure, stroke, etc.. Machine learning is the study of computer algorithms. In this work by adopting Machine learning algorithms such as Logistic Regression, Decision Tree, SVM[Support Vector Machine]are done to foresee the Cardiac Arrhythmia. The data-sets are collected from UCI Repository & processed using python programming .From all the three applied algorithms the SVM model showed the better results of 91.41\% in terms of accuracy for 80/20 combinations of Train and Test data sets. Therefore from this work SVM model is considered as best algorithm for the prediction of Cardiac Arrhythmia.
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Kavyashree et al. (2022) studied Cardiac Arrhythmia. Support Vector Machine (SVM) vs. Logistic Regression and Decision Tree was evaluated on Prediction accuracy. The Support Vector Machine (SVM) model achieved the highest accuracy of 91.41% in predicting cardiac arrhythmia using an 80/20 train-test data split.
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