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All issues Volume 392 (2024) MATEC Web Conf., 392 (2024) 01142 References
Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01142
Number of page(s) 9
DOI https://doi.org/10.1051/matecconf/202439201142
Published online 18 March 2024
  1. P. Manickam, S.A. Mariappan, S.M. Murugesan, S. Hansda, A. Kaushik, R. Shinde, S.P. Thipperudraswamy. Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors, 12, 8, 1-29, (2022) [Google Scholar]
  2. M.A. Moreno-Ibarra, Y. Villuendas-Rey, M.D. Lytras, C. Yáñez-Márquez, J.C. Salgado-Ramírez. Classification of diseases using machine learning algorithms: A comparative study. Mathematics, 9, 15, 1-21, (2021) [Google Scholar]
  3. J. Ngo, B.G. Hwang, C. Zhang. Factor-based big data and predictive analytics capability assessment tool for the construction industry. Autom. Constr., 110, (2020) [Google Scholar]
  4. M. Alkhodari, L. Fraiwan. Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. Comput Methods Programs Biomed., 200, (2021) [Google Scholar]
  5. T. Poongodi, R. Krishnamurthi, R. Indrakumari, P. Suresh, B. Balusamy. Wearable devices and IoT. A handbook of Internet of Things in biomedical and cyber physical system, 245-273, (2020) [Google Scholar]
  6. J. Al-Jaroodi, N. Mohamed, E. Abukhousa. Health 4.0: on the way to realizing the healthcare of the future. IEEE Access, 8, 211189-211210, (2020) [Google Scholar]
  7. S. Robinson. Cardiovascular disease. In Priorities for Health Promotion and Public Health, 355-393, (2021) Routledge [CrossRef] [Google Scholar]
  8. M.M. Yaqoob, M. Nazir, M. A. Khan, S. Qureshi, A. Al-Rasheed. Hybrid classifierbased federated learning in health service providers for cardiovascular disease prediction. Appl. Sci., 13, 3, 1-17, (2023) [Google Scholar]
  9. M. Umer, T. Aljrees, H. Karamti, A. Ishaq, S. Alsubai, M. Omar, I. Ashraf. Heart failure patients monitoring using IoT-based remote monitoring system. Sci. Rep., 13, 1, 1-19, (2023) [CrossRef] [Google Scholar]
  10. S. Singh, A. Singh, S. Limkar. Prediction of Heart Disease Using Deep Learning and Internet of Medical Things. Int. J. Intell. Syst. Appl. Eng., 12, 1s, 512-525, (2024) [Google Scholar]
  11. T. Abbas, A. Fatima, T. Shahzad, K. Alissa, T.M. Ghazal, M.M. Al-Sakhnini, A. Ahmed. Secure IoMT for disease prediction empowered with transfer learning in healthcare 5.0, the concept and case study. IEEE Access, 11, 39418-39430, (2023) [Google Scholar]
  12. M. Talha, R. Mumtaz, A. Rafay. Paving the way to cardiovascular health monitoring using Internet of Medical Things and Edge-AI. In IEEE 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2), 1-6, (2022) [Google Scholar]
  13. A. Petreska, B. Ristevski, D. Slavkovska, S. Nikolovski, P. Spirov, N. Rendevski, S. Savoska. Machine Learning Algorithms for Heart Disease Prognosis using IoMT Devices, (2023) [Google Scholar]
  14. K.S. Adewole, A.G. Akintola, R.G. Jimoh, M.A. Mabayoje, M.K. Jimoh, F.E. Usman-Hamza, A.O. Ameen. Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care. In Intelligent IoT Systems in Personalized Health Care, 105-145, (2021) Academic Press [Google Scholar]
  15. K.K. Baseer, K. Sivakumar, D. Veeraiah, G. Chhabra, P.K. Lakineni, M.J. Pasha, G. Harikrishnan. Healthcare diagnostics with an adaptive deep learning model integrated with the Internet of medical Things (IoMT) for predicting heart disease. Biomed. Signal Process. Control., 92, (2024) [Google Scholar]
  16. T.R. Ramesh, U.K. Lilhore, M. Poongodi, S. Simaiya, A. Kaur, M. Hamdi. Predictive analysis of heart diseases with machine learning approaches. Malays. J. Comput. Sci., 132-148, (2022) [Google Scholar]
  17. http://archive.ics.uci.edu/ml/datasets.php [Google Scholar]

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