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

Ahmad F. Subahi, Osamah Ibrahim Khalaf, Youseef Alotaibi, Rajesh Natarajan (), Natesh Mahadev and Timmarasu Ramesh
Additional contact information
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

Sustainability, 2022, vol. 14, issue 21, 1-20

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.

Keywords: heart disease (HD); Internet of Things (IoT); wearable sensors; healthcare; electrocardiogram (ECG); Modified Self-Adaptive Bayesian algorithm (MSABA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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