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

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.

An Efficient Healthcare Monitoring System for Cardiovascular Diseases using Deep Modified Neural Networks

“…MANAGE-HF [11] 58.00 GA [11] 65.00 LR [20] 72.738 ML [11] 74.00 XGB [3] 75.998 RNN [13] 76.04 CNN [20] 76.7 KNN [25] 78.00 GNBC [5] 81.00 QGNBC [5] 82.00 RFC [5] 83.00 DTC [5] 84.00 ANN [2] 85.00…”

“…DT [19] 75.00 ML [11] 78.00 DTC [5] 79.00 QDTC [5] 80.00 KNN [25] 83.00 ANN [2] 84.0336 GNBC [5] 85.00 SVM [14] 85.00 VQC [9] 85.00 QGNBC [5] 86.00 QKNN [5] 86.00 XGB [3] 86.16…”

Design of a Telepresence Robot to Avoid Obstacles in IoT-Enabled Sustainable Healthcare Systems

“…Before starting the hardware design, the mobile robot's actuators and sensors operations are worth mentioning; they are the main actuators of the first two pre-built engines. First, the direct-current (DC) motor for driving TruckPuller2 of 7.2 V and the other in the longitudinal direction of the servo motor is used (HiTec HS-5745MG [13]) for steering. It is complemented by one red and one green light-emitting diode (LED) to indicate the state of the robot later.…”

“…In particular, a microcontroller-based control was developed to control the robot's behavior depending on user preferences and environmental influences and controls. The robot chassis was modified as per the designed control actuators and sensors [12][13][14][15]; it included small, necessary mechanical structures. We intend to develop a control program to evaluate the measured values of various sensors, and then to send appropriate control variables to the actuators.…”

A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models

“…ML approaches have evolved as a more effective means of assisting the healthcare sector in the diagnosis of CVD, and they have been employed in the literature for CVD prediction and classification [ 12 , 13 ]. However, ML methods still require the manual use of engineering techniques for feature extraction, feature selection, and feature reduction [ 14 ]. Therefore, the major drawbacks of such techniques are to identify and use the appropriate features from the patient data.…”