Effective cardiac disease classification using FS-XGB and GWO approach

Abstract

Globally, cardiovascular diseases (CVDs) are a leading cause of death; however, their impact can be greatly mitigated by early detection and treatment. Machine learning (ML)-based algorithms that use features extracted from electrocardiogram (ECG) signals are known to provide good accuracy in predicting various CVDs. Thus, in order to build more effective and efficient machine learning models, it is necessary to extract significant features from ECGs. In order to reduce overfitting and training overhead and improve model performance even more, feature selection or dimensionality reduction is essential. In this regard, the current work uses the grey wolf optimization (GWO) technique to pick a reduced feature set after extracting pertinent characteristics from ECG signals in order to identify five different types of CVDs. On the basis of the feature relevance of the chosen features, a feature-specific extreme gradient boosting approach (FS-XGB) is also suggested. The suggested FS-XGB classifier's performance is contrasted with that of other machine learning techniques, including gradient boosting method, AdaBoost, naïve Bayes, and support vector machine (SVM). The proposed methodology achieves a maximum classification accuracy, precision, recall, F1-score, and AUC value of 98.8 %, 100 %, 99.8 %, 100 %, and 98.8 %, respectively, with just seven optimal features, significantly fewer than the number of features used in existing works.