Research Article
IoT-Based Classification of COVID-19 Cases with Cardiovascular Disease Using Deep Convolutional Decision Trees
@INPROCEEDINGS{10.1007/978-3-031-81168-5_19, author={R. Amudha and M. S. Kavitha and S. Karthik and Balakrishnan Biju}, title={IoT-Based Classification of COVID-19 Cases with Cardiovascular Disease Using Deep Convolutional Decision Trees}, proceedings={Broadband Communications, Networks, and Systems. 14th EAI International Conference, BROADNETS 2024, Hyderabad, India, February 16--17, 2024, Proceedings, Part I}, proceedings_a={BROADNETS}, year={2025}, month={2}, keywords={IoT COVID-19 cardiovascular disease deep learning convolutional decision trees}, doi={10.1007/978-3-031-81168-5_19} }- R. Amudha
M. S. Kavitha
S. Karthik
Balakrishnan Biju
Year: 2025
IoT-Based Classification of COVID-19 Cases with Cardiovascular Disease Using Deep Convolutional Decision Trees
BROADNETS
Springer
DOI: 10.1007/978-3-031-81168-5_19
Abstract
The COVID-19 has underscored the need for advanced healthcare solutions. This research addresses the intersection of COVID-19 and cardiovascular disease (CVD) through the lens of Internet of Things (IoT) and deep learning. Patients with pre- existing cardiovascular conditions face elevated risks when infected with COVID-19. Current diagnostic methods often lack the precision required to identify specific health risks in this vulnerable population. This research aims to bridge this gap by developing a sophisticated model that combines IoT data and deep learning techniques for robust classification of COVID-19 cases with concurrent cardiovascular disease. While existing studies explore either COVID-19 classification or the relationship with cardiovascular conditions separately, there is a noticeable research gap in the integration of IoT and deep convolutional decision trees for a comprehensive analysis. This study fills this void by proposing a novel approach that harnesses the potential of both technologies to improve diagnostic accuracy. With the aim of enhancing classification accuracy, we propose an IoT-based framework that leverages deep convolutional decision trees. Our methodology involves the collection of diverse IoT data streams, including vital signs and patient activity, to create a comprehensive dataset. Deep convolutional decision trees are then employed to extract intricate patterns and relationships from the data. The model is trained on a well-curated dataset, optimizing its ability to accurately classify COVID-19 cases in individuals with pre-existing cardiovascular conditions. The results demonstrate a significant improvement in classification accuracy compared to traditional methods. The model exhibits enhanced sensitivity and specificity, showcasing its potential for early and precise identification of COVID-19 cases in individuals with cardiovascular disease.
