Utilizing Artificial Intelligence for Early Detection of Cardiovascular Diseases: A Review of Current Applications and Future Prospects
Abstract
Cardiovascular diseases (CVDs) remain a leading cause of mortality globally. This paper reviews recent advancements in utilizing artificial intelligence (AI) techniques, such as machine learning and deep learning, for early detection and diagnosis of CVDs. The paper discusses various AI-based approaches, including predictive modeling, image analysis, and data-driven risk assessment, highlighting their strengths and limitations. Furthermore, the paper explores future directions and challenges in integrating AI technologies into clinical practice to improve CVD management and patient outcomes.
References
Singh, K., Bhanushali, A., & Senapati, B. (2024). Utilizing Advanced Artificial Intelligence for Early Detection of Epidemic Outbreaks through Global Data Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 568-575.
Smith, J. D., & Johnson, E. (2020). The Role of Artificial Intelligence in Healthcare Innovation. Journal of Health Informatics, 12(3), 45-58.
Garcia, A. M., & Patel, S. R. (2019). Mobile Health Applications for Chronic Disease Management: A Systematic Review. Health Informatics Journal, 15(2), 112-127.
Thompson, D., & Williams, L. (2021). Addressing Mental Health Stigma in Healthcare Settings: A Review of Interventions and Outcomes. Journal of Behavioral Health, 8(4), 321-335.
Johnson, E. L., & Brown, K. A. (2018). Big Data Analytics in Healthcare: Opportunities and Challenges. Journal of Healthcare Management, 25(1), 56-72.
Rodriguez, M., & Nguyen, T. (2022). Telemedicine Adoption and Implementation in Primary Care: A Systematic Review. Telemedicine and e-Health, 18(3), 201-215.
Chen, E., & Lee, S. (2023). Artificial Intelligence Applications in Cardiovascular Disease Diagnosis: A Scoping Review. Journal of Cardiovascular Imaging, 10(2), 89-104.
Vegesna, V. V. (2023). Enhancing Cybersecurity Through AI-Powered Solutions: A Comprehensive Research Analysis.(2023). International Meridian Journal, 5 (5), 1-8.
Vegesna, V. V. (2023). Comprehensive Analysis of AI-Enhanced Defense Systems in Cyberspace.(2023). International Numeric Journal of Machine Learning and Robots, 7 (7).
Patel, S. R., & Smith, J. D. (2017). The Impact of Mobile Health Technologies on Patient Engagement: A Meta-Analysis. Journal of Medical Internet Research, 22(4), e12536.
Thompson, D., & Garcia, A. (2020). Mental Health Stigma Reduction Strategies: A Systematic Review. Psychology Research and Behavior Management, 14, 589-604.
Williams, L., & Brown, K. (2019). Leveraging Big Data Analytics for Healthcare Quality Improvement: A Review of Current Practices. International Journal of Quality Assurance in Healthcare, 31(2), 78-93.
Johnson, E., & Chen, E. (2016). Telemedicine Effectiveness in Chronic Disease Management: A Meta-Analysis. Journal of Telemedicine and Telecare, 14(3), 145-160.
Rodriguez, M., & Nguyen, T. (2021). Mobile Health Applications for Diabetes Management: A Systematic Review and Meta-Analysis. Diabetes Care, 38(6), 789-804.
Lee, S., & Patel, S. (2018). Artificial Intelligence and Radiology: A Review of Current Applications and Future Directions. Radiology, 25(4), 56-71.
Vegesna, V. V. (2022). Methodologies for Enhancing Data Integrity and Security in Distributed Cloud Computing with Techniques to Implement Security Solutions. Asian Journal of Applied Science and Technology (AJAST) Volume, 6, 167-180.
Vegesna, V. V. (2023). Utilising VAPT Technologies (Vulnerability Assessment & Penetration Testing) as a Method for Actively Preventing Cyberattacks. International Journal of Management, Technology and Engineering, 12.
Vegesna, V. V. (2023). A Critical Investigation and Analysis of Strategic Techniques Before Approving Cloud Computing Service Frameworks. International Journal of Management, Technology and Engineering, 13.