Improved stacked ensemble with genetic algorithm for automatic ECG diagnosis of children living in high-altitude areas

Na Zhao, Xiaopeng Li, Yaofei Ma, Hao Wang, Shin-Jye Lee, Jian Wang

Abstract: Highlights•For the first time, we conducted a study on the automatic diagnosis of pediatric ECGs in high-altitude areas.•We developed a model that combines a GA and the stacked ensemble method, capable of differentiating between normal and abnormal ECGs and classify four types of rhythm.•We incorporated not only popular ML models but also the most successful deep learning models for tabular data tasks in recent years into the baseline models, enabling us to demonstrate the advantages of the developed model.•We conducted a characteristic importance analysis, revealing important factors contributing to ECG abnormalities in children living in high-altitude regions.