Combining Machine Learning and Bayesian networks for Decision Support in Arrythmia Diagnosis
27We propose an architecture for a personal health agent (PHA) that combines machine learning and a Bayesian network for detecting and diagnosing arrhythmia based on electrocardiogram (ECG) characteristics. Focusis placed on atrial fibrillation (AF), the commonest type of arrhythmia. Machine learning is used for classifying the ECG signal. The absence of a Pwave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP was the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The presence or absence of a P-wave as determined by the ML model is input into the BN.
In a bid to extend this work, instead of a binary classification of the ECG signal based on the presence or absence of a P-wave, we classify an ECG signal as either atrial fibrillation, other arrhythmia, or no arrhythmia. Four ML models, i.e., gradient boosting, random forest, multilayer perceptron and support vector machine, are compared and evaluated using a dataset of 5,340 records containing 12-lead ECG signals created from the Chapman-Shaoxing database. Among the four models, the gradient boosting model produces the best accuracy of 82.88%. The detected pattern is integrated into a BN that captures expert knowledge about the causes of arrhythmia. The agent has the ability to guide the diagnosis process. It suggests what questions to ask to increase certainty in the presence of arrhythmia, and what arrhythmia causes to follow up. The architecture is evaluated using application use cases.
Speaker: Tezira Wanyana, University of Cape Town, Mbarara University of Science and Technology
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