Key result
An improved stacked ensemble model with a genetic algorithm exhibited better accuracy, recall, and F1 score for automatic pediatric ECG diagnosis compared to commonly used classification methods.
Why the study?
Heart disease incidence is higher among children in high-altitude areas, yet research and technology focusing on automatic ECG diagnosis for children in these regions are lacking.
Does an improved stacked ensemble model with genetic algorithm improve automatic ECG diagnosis in children living in high-altitude areas compared to commonly used classification methods?
Does an improved stacked ensemble model with genetic algorithm improve automatic ECG diagnosis in children living in high-altitude areas compared to commonly used classification methods?
An improved stacked ensemble model with a genetic algorithm demonstrates superior performance in the automatic diagnosis and classification of pediatric ECGs from high-altitude regions.
Should not yet change pediatric ECG practice in high-altitude regions; leaves open need for prospective validation of ensemble models.
Electrocardiogram (ECG) is a commonly used diagnostic tool in clinical practice that plays a vital role in the diagnosis and treatment of various heart diseases. Previous studies have indicated that the incidence of heart disease in children living in high-altitude areas is significantly higher than those in low-altitude areas, which increases the demand for ECG examinations in these regions. However, there is a lack of research and technology that focus on automatic ECG diagnosis for children in high-altitude areas. This study utilized electronic medical records of pediatric electrocardiograms (ECGs) from high-altitude areas in Yunnan Province, China as research data. An improved genetic algorithm (GA) was employed to find the best combination of base classifiers, and a stacked ensemble approach was utilized to develop a reliable model for automatic diagnosis of pediatric ECGs. The developed model is capable of identifying abnormal ECGs and classifying heart rhythm types into four categories: normal sinus rhythm, sinus tachycardia, sinus bradycardia, and other arrhythmias. The model was tested and compared with commonly used classification methods. The results show that the model developed in this paper exhibits better performance in terms of accuracy, recall, and F1 score. The model’s classification effectiveness was further demonstrated by creating a receiver operating characteristic (ROC) curve. Finally, feature importance analysis highlighted the significance heart rate, developmental stage, and QRS complex axis deviation angle in the model.
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Zhao et al. (2023) studied Heart disease in children living in high-altitude areas. Improved stacked ensemble with genetic algorithm vs. Commonly used classification methods was evaluated on Accuracy, recall, and F1 score for identifying abnormal ECGs and classifying heart rhythm types. An improved stacked ensemble model with a genetic algorithm exhibited better accuracy, recall, and F1 score for automatic pediatric ECG diagnosis compared to commonly used classification methods.
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