Key result
A modular neural network model achieved up to 82.22% testing classification accuracy in classifying normal and abnormal cardiac arrhythmias using the UCI Arrhythmia dataset.
A modular neural network model can classify cardiac arrhythmias from ECG data with up to 82.22% accuracy.
Supports AI arrhythmia classification development; leaves open prospective clinical validation before practice adoption.
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method uses Modular neural network (MNN) model to classify arrhythmia into normal and abnormal classes. We have performed experiments on UCI Arrhythmia data set. Missing attribute values of this data set are replaced by closest column value of the concern class. We have constructed neural network model by varying number of hidden layers from one to three and are trained by varying training percentage in data set partitions. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). The experimental results presented in this paper show that up to 82.22% testing classification accuracy can be obtained.
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Jadhav et al. (2010) studied Cardiac arrhythmia (n=452). Modular neural network (MNN) model was evaluated on Testing classification accuracy. A modular neural network model achieved up to 82.22% testing classification accuracy in classifying normal and abnormal cardiac arrhythmias using the UCI Arrhythmia dataset.