A Personal Health Agent for Decision Support in Arrhythmia Diagnosis

Tezira Wanyana, Mbithe Nzomo, C. Sue Price, Deshendran Moodley

Abstract: We propose an architecture for a personal health agent (PHA) that combines machine learning and a Bayesian network (BN) for detecting and diagnosing heart disease, specifically arrhythmia. Machine learning (ML) is used for classifying a patient’s ECG signal. 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% when classifying an ECG signal as either atrial fibrillation, other arrhythmia, or no arrhythmia. The detected pattern is integrated into a BN that captures expert knowledge about the causes of arrhythmia. The BN structure and parameters are informed by expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s framework. The agent uses a decision support module to guide the diagnosis process. It suggests what questions to ask to increase certainty of the presence of arrhythmia, and it suggests what arrhythmia causes to follow up. This is achieved using sensitivity analysis and diagnostic Bayesian reasoning respectively. The architecture is evaluated using application use cases.

External IDs:doi:10.1007/978-3-031-37496-8_20