Abstract
Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson’s (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson’s disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson’s disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson’s disease prediction. The proposed model produced accuracy for the Parkinson’s disease prediction has outperformed its closest competitors in all algorithms by 7.3%.
Highlights
The Internet of Things, or IoT, is a world that is full of sensors and actuators, robots, and computers that are connected and able to communicate with larger networks
The first experiment used to evaluate the efficiency and effectiveness of the proposed approach in achieving a minimal error utilizing five datasets that obtained from the archive of University of California-Irvine (UCI) (University of California, Irvine) [62]
PERFORMANCE EVALUATION OF THE MODELS Several measurements utilized to determine the efficiency of the suggested adaptive neuro-fuzzy inference system (ANFIS)-PSO and GWO (PSOGWO) approach and to check the efficiency of the performance of the solutions, which described as follows [63]: 1. Mean square error (MSE): 1 mean square error (MSE) =
Summary
The Internet of Things, or IoT, is a world that is full of sensors and actuators, robots, and computers that are connected and able to communicate with larger networks. IoT can transform the routine, regular devices into intelligent devices. These digitally connected devices in the medical and healthcare sector are receiving a lot of traction [1]. Integrating IoT with medical applications has increased the efficiency of remote health monitoring systems for the elderly or chronically affected patients in need of long-term personal service [2]. A vast amount of data continually is provided by the medical sensors or wearable devices in the IoT health systems. The data generated by IoT sensors are high-speed.
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