The Impact of Class Weight Optimization on Improving Machine Learning Outcomes in Identifying COVID-19 Specific ECG Patterns

Sara Khan, Walaa Ismail, Shada Alsalamah, Ebtesam Mohamed, Hessah A. Alsalamah, Hessah A. Alsalamah

2024

Abstract

The Covid-19 pandemic has resulted in 550 million cases and 6.3 million fatalities, with the virus severely affecting the lungs and cardiovascular system. A study utilizes a VGG16 model adapted for a 12-Lead ECG Image database to assess the disease’s impact on cardiovascular health. The research addresses the challenge of data imbalance by experimenting with different training approaches: using balanced datasets, imbalanced datasets, and class weight adjustments for imbalanced datasets. These models are designed for a three-class multiclass classification of ECG images: Abnormal, Covid-19, and Normal categories. Performance evaluations, including accuracy scores, confusion matrices, and classification reports, show promising results. The model trained on a balanced dataset achieved a 90% accuracy rate. When trained on an imbalanced dataset, the accuracy dropped to 82%. However, with class weight adjustments, the accuracy rebounded to 87%. The study proves that the adapted VGG16 model can effectively handle both balanced and imbalanced datasets. Further testing and enhancements can be carried out using additional datasets, making it a valuable tool for understanding the cardiovascular implications of Covid-19.

Paper Citation

in Harvard Style

Khan S., Ismail W., Alsalamah S., Mohamed E. and A. Alsalamah H. (2024). The Impact of Class Weight Optimization on Improving Machine Learning Outcomes in Identifying COVID-19 Specific ECG Patterns. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 562-567. DOI: 10.5220/0012413100003657

in Bibtex Style

@conference{healthinf24,
author={Sara Khan and Walaa Ismail and Shada Alsalamah and Ebtesam Mohamed and Hessah A. Alsalamah},
title={The Impact of Class Weight Optimization on Improving Machine Learning Outcomes in Identifying COVID-19 Specific ECG Patterns},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={562-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012413100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - The Impact of Class Weight Optimization on Improving Machine Learning Outcomes in Identifying COVID-19 Specific ECG Patterns
SN - 978-989-758-688-0
AU - Khan S.
AU - Ismail W.
AU - Alsalamah S.
AU - Mohamed E.
AU - A. Alsalamah H.
PY - 2024
SP - 562
EP - 567
DO - 10.5220/0012413100003657
PB - SciTePress