Omar M. El-Habbak¹, Abdelrahman M. Abdelalim¹, Nour H. Mohamed¹, Habiba M. Abd-Elaty¹, Mostafa A. Hammouda¹, Yasmeen Y. Mohamed¹, Mohanad A. Taifor¹, Ali W. Mohamed^2,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2953-2969, 2022, DOI:10.32604/cmc.2022.020109

Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by… More >

Rajalakshmi Shenbaga Moorthy^1,*, P. Pabitha²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3101-3119, 2021, DOI:10.32604/cmc.2021.016489

Abstract Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression. This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network (IRBFNN). Particle swarm optimization (PSO) with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN. The performance of RBFNN is seriously affected by the centers of hidden neurons. Conventionally K-means was used to find the centers of hidden neurons. The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.… More >

Haixia Yang¹, Yixian Shen², Wei Zhuang², Chunming Gao³, Dong Dai⁴, Weigong Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1217-1238, 2021, DOI:10.32604/cmes.2021.014558

Abstract Parkinson’s disease (PD) is a very common neurodegenerative disease that occurs mostly in the elderly. There are many main clinical manifestations of PD, such as tremor, bradykinesia, muscle rigidity, etc. Based on the current research on PD, the accurate and convenient detection of early symptoms is the key to detect PD. With the development of microelectronic and sensor technology, it is much easier to measure the barely noticeable tremor in just one hand for the early detection of Parkinson’s disease. In this paper, we present a smart wearable device for detecting hand tremor, in which MPU6050 (MIDI Processing Unit) consisting… More >

EMILIA MANOLE^1,2,#, LAURA DUMITRESCU^2,3,#, CRISTINA NICULIȚE^1,3, BOGDAN OVIDIU POPESCU^1,2,3, LAURA CRISTINA CEAFALAN^1,3,*

BIOCELL, Vol.45, No.1, pp. 1-16, 2021, DOI:10.32604/biocell.2021.013452

Abstract An increasing number of studies provide evidence for the existence of a microbiota-gut-brain axis and its potential involvement in the development of sporadic Parkinson’s disease and other neurodegenerative conditions. The neuropathologic hallmark of Parkinson’s disease is the presence of brain intraneuronal aggregates of misfolded alpha-synuclein, known as Lewy bodies. Some gut microbiota products may trigger alpha-synuclein conformational changes in the neurons of the enteric nervous system, which can then spread to the brain in a prion-like fashion through the vagus nerve. Others may interfere with neuroinflammatory pathways and susceptibility to neurodegeneration. In this review, we assess the potential role of… More >

Kemal Akyol^{1, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 619-632, 2020, DOI:10.32604/cmes.2020.07632

Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order… More >

Somayeh Hessam¹, Shaghayegh Vahdat¹, Irvan Masoudi Asl^2,*, Mahnaz Kazemipoor³, Atefeh Aghaei⁴, Shahaboddin Shamshirband,^5,6,*, Timon Rabczuk⁷

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 11-26, 2019, DOI:10.32604/cmc.2019.06472

Abstract In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The… More >