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Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

Author

Listed:
  • Ahmad F. Subahi

    (Department of Computer Science, University College of Al Jamoum, Umm Al-Qura University, Makkah 21421, Saudi Arabia)

  • Osamah Ibrahim Khalaf

    (Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad 64074, Iraq)

  • Youseef Alotaibi

    (Department of Computer Science, College of Computer and Information Systems, Umm Al Qura University, Makkah 21421, Saudi Arabia)

  • Rajesh Natarajan

    (Information Technology Department, University of Technology and Applied Sciences-Shinas, Al-Aqr, Shinas 324, Oman)

  • Natesh Mahadev

    (Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore 570002, India)

  • Timmarasu Ramesh

    (Department of Computer Science and Engineering, Presidency University, Bangalore 560064, India)

Abstract

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.

Suggested Citation

  • Ahmad F. Subahi & Osamah Ibrahim Khalaf & Youseef Alotaibi & Rajesh Natarajan & Natesh Mahadev & Timmarasu Ramesh, 2022. "Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14208-:d:958860
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    References listed on IDEAS

    as
    1. Adlin Sheeba & S. Padmakala & C. A. Subasini & S. P. Karuppiah, 2022. "MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 25(10), pages 1180-1194, July.
    2. D. Deepika & N. Balaji, 2022. "Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 25(12), pages 1409-1427, August.
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    Cited by:

    1. Fawad Naseer & Muhammad Nasir Khan & Ali Altalbe, 2023. "Telepresence Robot with DRL Assisted Delay Compensation in IoT-Enabled Sustainable Healthcare Environment," Sustainability, MDPI, vol. 15(4), pages 1-15, February.

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