Comparison of LightGBM With XGBoost Algorithms in Determining Arrhythmia Classification in Students

Delima Sitanggang; Eddrick Wilbert Solo; Ferdy Immanuel Sinaga; Stefanus Jorgi L.Tobing; Feliks Daniel  Hutasoit; Agung Prabowo

doi:10.52436/1.jutif.2025.6.4.5015

Authors

Delima Sitanggang Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia
Eddrick Wilbert Solo Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia
Ferdy Immanuel Sinaga Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia
Stefanus Jorgi L.Tobing Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia
Feliks Daniel Hutasoit Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia
Agung Prabowo Faculty of Science and Technology, Universitas Prima Indonesia, Medan, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.4.5015

Keywords:

Arrhythmia, Electrocardiogram, LightGBM, Machine Learning, XGBoost

Abstract

Arrhythmia is a heart rhythm disorder that may occur unpredictably with life-threatening risk if it were not treated immediately. This heart disorder generally affects the elderly, but symptoms of this disorder can also arise in children and adolescents, especially for those with heart problems or are often under stress. The implementation of this research is aimed at analyzing the symptoms of early arrhythmia in adolescent children using electrocardiogram signals. In order to obtain the best possible results in determining the higher performing algorithm, two machine learning methods were used to predict the classification of arrhythmia which will be compared for their accuracy. The subjects of this study included 106 students from SMK Swasta Teladan Sumatera Utara 2 located in the city of Medan, of which 72 final subject data were used to train the capability of both models used to predict arrhythmia classification categorized into four categories, namely normal, abnormal, potential of arrhythmia, and high potential of arrhythmia. The LightGBM model outperformed the XGBoost model, with 95.11% accuracy and 95.03% F1 Score, and although the loss value of the LightGBM model is higher than the loss value of the XGBoost model, the difference between these two values is negligible and the loss value of LightGBM can be considered as excellent with a value of 0.1503. This research contributes to the advancement of digital health by demonstrating the potential of machine learning-based ECG analysis for highly accurate early arrhythmia detection in adolescent, non-clinical populations.

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