Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, for the miss-valued attributes, we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes. However, in order to ensure the model optimality, we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers. The preprocessing step led to 161 out of 279 attributes (features). Thereafter, a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms. In short, our study comprises three main blocks: (1) sensing data and preprocessing; (2) feature queuing, selection, and extraction; and (3) the predictive model. Our proposed method improves classification performance in terms of accuracy, F1 measure, recall, and precision when compared to state-of-the-art techniques. It achieves 98.5% accuracy for binary class mode and 98.9% accuracy for categorized class mode.