Adaptive synthetic-based arrhythmia classification using machine learning techniques

Abstract

Cardiac arrhythmias, characterized by irregular heart rhythms, pose a significant challenge for timely and accurate diagnosis. This paper presents an advanced framework for arrhythmia classification that addresses the class imbalance issue using Adaptive Synthetic (ADASYN) sampling combined with a comprehensive set of machine learning techniques. We implemented various classifiers, including Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron, Gradient Boosting, AdaBoost, Light Gradient Boosting, CatBoost, and Extreme Gradient Boosting. Our experimental results demonstrate that Random Forest, Gradient Boosting, Light Gradient Boosting, and XGBoost achieved a remarkable accuracy of 97%. Other models, such as Decision Tree and Logistic Regression, also performed well, achieving 95% and 94% accuracy, respectively. KNN and Naive Bayes yielded 93% and 81% accuracy, respectively, while AdaBoost underperformed with an accuracy of 24%. Precision scores across the models remained high, except for Naive Bayes and AdaBoost. All models, except AdaBoost, demonstrated excellent recall. Our proposed methodology outperforms previous works, setting a new benchmark for arrhythmia classification. These findings emphasize the effectiveness of integrating ADASYN with machine learning techniques to enhance arrhythmia detection, with significant potential for improving clinical diagnostic processes and patient outcomes.