Machine Learning for Aortic Stenosis: Enhancing Diagnostic Accuracy and Security in Health Information Systems
1 Faculty of Computer Science and Engineering, Patuakhali Science and Technology University, Patuakhali, Bangladesh.
2 College of Business, Westcliff University, Irvine, CA 92614, USA.
3 Department of Aquaculture and Fisheries, University of Arkansas at Pine Bluff, AR-71601, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(02), 1940–1945
Article DOI: 10.30574/wjarr.2024.24.2.3577
Publication history:
Received on 30 September 2024; revised on 18 November 2024; accepted on 20 November 2024
Abstract:
Aortic Stenosis (AS) is a prevalent and potentially life-threatening cardiovascular condition that requires accurate diagnosis for optimal management. Traditional diagnostic methods, while effective, face limitations in terms of precision, timely detection, and clinician workload. The emergence of Machine Learning (ML) offers an innovative solution to these challenges, enhancing diagnostic accuracy and improving patient outcomes. This article explores how ML algorithms can be utilized to refine AS diagnosis, particularly through medical imaging and predictive modeling. In addition, the integration of ML in health information systems must be coupled with robust data security measures to protect sensitive patient information. We discuss the intersection of machine learning and healthcare IT security, focusing on innovative methods for safeguarding health data while improving diagnostic efficiency. The paper examines various ML techniques applied to AS, evaluates their impact on clinical workflows, and identifies the security protocols necessary to ensure compliance with privacy regulations. Finally, the study presents the potential challenges and future directions for integrating ML and health information security in clinical practice.
Keywords:
Machine Learning; Aortic Stenosis; Data Security; Artificial Intelligence (AI); Healthcare Data Protection; Predictive Analytics
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0