iCardo 3.0: ECG-Based Prediction of Conduction Disturbances Using Demographic Features

Cardiovascular disease (CVD) is one of the most contributing diseases to premature mortality around the globe. Low- and middle-income countries like India account for almost 80% of global CVD fatalities. Predicting cardiovascular diseases at an early stage can improve the quality of life. Electrocardiography (ECG) is one of the non-invasive methods to assess heart function disorders and CVDs. The paper presents the prediction of conduction disturbance or disorders (CD) through a 12-lead electrocardiogram (ECG) that leads to chronic heart failure or cardiac arrest. Three ensemble machine learning models i.e., random forest (RF), XGBoost, and the support vector machine, are used to classify the conduction disturbance subjects from the ‘normal’ subjects. In addition to this, the paper also presents a comparative study to show the effect of two demographic features, ‘age’ and ‘sex’ on the prediction of conduction disturbance’s subjects. The performance of the classifiers’ is measured in terms of accuracy, precision, recall, and F1 score. Tenfold cross-validation is utilised, and the receiver operating curve is traced for each of the combinations of tenfold cross-validation. The performance is measured with a confusion matrix for all three classifiers. The performance with RF and XGBoost performance is similar in terms of accuracy, whereas the total number of true predictions is higher in the case of RF. The proposed model would be useful for continuous monitoring and prediction conduction disturbance in the smart healthcare framework.