Intelligent healthcare for cardiac patients utilizing neural networks, k-means clustering, and ad hoc routing
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Abstract
This paper introduced a comprehensive healthcare system designed to address the unique challenges faced by cardiac patients in economically and geographically constrained regions. The system operated in three phases, each contributing to the effective care of patients. In the initial phase, an artificial neural network model identified potential cardiac patients with an impressive accuracy of 90.16%, demonstrating its potential for early detection. The second phase employed k-means clustering to categorize patients into three groups based on the severity of their condition, facilitating precise prognosis and stratification. Finally, the system utilized an innovative ad hoc routing algorithm to securely transmit patient data to remote servers, enabling expert monitoring and consultation. The study's outcomes demonstrate the system's ability to accurately identify at-risk patients, appropriately categorize their condition, and efficiently route critical data. This holistic approach leverages cutting-edge technologies and methodologies to transform healthcare delivery in underserved areas. The novel system presents a promising avenue for enhancing cardiac care in regions with limited access to advanced healthcare services, ultimately improving patient outcomes and reducing disparities in cardiovascular healthcare.
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