Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
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RESEARCH ARTICLE   (Open Access)
Contents Vol 8 (9)

ATC-CNN-Based IoT Framework for Real-Time Cardiovascular Monitoring: A Comparative Analysis with Deep Learning Models

Dev Ras Pandey 1*, Ahilya Dubey 1

+ Author Affiliations

Journal of Angiotherapy 8 (9) 1-6 https://doi.org/10.25163/angiotherapy.899878

Submitted: 27 June 2024 Revised: 06 September 2024  Published: 09 September 2024 

Abstract

Background: Cardiovascular diseases (CVDs), including hypertension, coronary heart disease, and ventricular fibrillation, remain the leading cause of death worldwide. The COVID-19 pandemic accelerated the adoption of telecardiology to minimize in-person interactions, enhancing access to cardiovascular care, especially in remote areas. However, traditional real-time monitoring (RTM) systems often require patient visits, creating a need for cost-effective, user-friendly alternatives that leverage IoT and AI for improved patient management. Methods: This study developed an IoT-enabled RTM system integrated with an Adaptive Thresholding Classifier-Convolutional Neural Network (ATC-CNN) framework for managing cardiovascular patients (CP). Data from a heart failure dataset in the UCI Machine Learning Archive was used to train and evaluate the model. Multiple deep learning (DL) algorithms, including MLP, CNN, LSTM, RNN, and the proposed ATC-CNN, were assessed based on accuracy, precision, recall, and F1 scores. Results: The ATC-CNN model achieved an accuracy of 0.9831, outperforming other DL models, including MLP, CNN, LSTM, and RNN. It demonstrated superior capabilities in handling complex cardiovascular data, offering reliable and timely diagnosis with high precision and recall. The integration of IoT and AI facilitated continuous monitoring and real-time data analysis, addressing key limitations of traditional RTM systems. Conclusion: The ATC-CNN framework shows great potential for revolutionizing cardiovascular care by enhancing remote monitoring capabilities and clinical decision-making. It provides an effective, real-time solution for early diagnosis and intervention, particularly in remote and underserved areas. Future research should focus on expanding datasets and refining the model for broader adaptability in diverse healthcare settings.

Keywords: Cardiovascular diseases, Real-time monitoring, IoT, AI, ATC-CNN

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