Application of neural networks in early detection and diagnosis of Parkinson's disease

Olanrewaju, Rashidah Funke; Sahari, Nur Syarafina; Musa, Aibinu Abiodun; Hakiem, Nashrul

doi:10.1109/citsm.2014.7042180

Cited by 25 publications

(9 citation statements)

References 6 publications

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“…The purpose of neural network training is to find weights w that minimize the error between the neural network output y and the expected output y. The normalized training loss is defined in (1).…”

Section: Problem Statement a Neural Network Trainingmentioning

confidence: 99%

“…In this paper, the position of the particle represents a solution to the weights w in the ANN. The fitness is evaluated based on (1). A swarm consists of N p particles.…”

Section: Pso Algorithmmentioning

confidence: 99%

“…That is to say, all work-items in a particle share the same weights, but correspond to different samples of data set. Every work-item evaluates (1), and all intermediate values and results are stored in private memory. The result computed by the workitem is transferred to the local memory of work-group, and each work-group sums the values using a reduction algorithm.…”

Section: ) Fitness Calculation Kernelmentioning

confidence: 99%

“…Recently, research on artificial neural network (ANN) has made great progress, and provides solutions for many practical problems in early detection [1], microwave circuit design [2], energy expenditure estimation [3], etc.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Acceleration of Hybrid PSO-QN Algorithm for Neural Network Training

Shi

Liu

et al. 2019

IEEE Access

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Artificial neural network (ANN) has successfully provided solutions to many practical problems. One of the difficulties in training ANNs is finding the ideal solution to the network weights quickly. This paper designs an implementation of the hybrid particle swarm optimization (PSO) and quasi-Newton (QN) algorithm on CPU-GPU platform using OpenCL to accelerate ANN training. The PSO-QN implementation combines the strength of the PSO algorithm in global search and the advantage of the QN algorithm in fast convergence rate. A configurable parallel line search implementation and an efficient parallel reinitialization implementation are proposed to improve the performance and reduce data transmission. Experiments show the PSO-QN hybrid parallel implementation on CPU-GPU platform can achieve up to 362x and 8.9x acceleration compared with the C++ implementations of PSO and BFGS-QN on CPU, respectively. Compared with the PSO and BFGS-QN parallel implementations, the training loss of the PSO-QN hybrid implementation at given training time is reduced by 15.16% and 3.97%, and the testing error is reduced by 13.66% and 6.86%, respectively. INDEX TERMS Heterogeneous computing, neural network training, PSO algorithm, quasi-Newton algorithm.

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Section: Problem Statement a Neural Network Trainingmentioning

confidence: 99%

“…In this paper, the position of the particle represents a solution to the weights w in the ANN. The fitness is evaluated based on (1). A swarm consists of N p particles.…”

Section: Pso Algorithmmentioning

confidence: 99%

Section: ) Fitness Calculation Kernelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Acceleration of Hybrid PSO-QN Algorithm for Neural Network Training

Shi

Liu

et al. 2019

IEEE Access

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“…They stated that among all three methods, decision tree has the lowest classification error and logistic regression model has second lowest, while ANN has a higher classification error. Geetha Ramani and G. Sivagami [7] provide a survey of data mining techniques that Rashidah et al [10] proposed a modelin early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. The output of the network is classified into healthy or PD by using K-Means…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson’s Disease Dataset on the Performance of Classifier Algorithms

Mohamed¹

2016

OALib

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Precise detection of PD is important in its early stages. Precise result can be achieved through data mining, classification techniques such as Naïve Bayes, support vector machine (SVM), multilayer perceptron neural network (MLP) and decision tree. In this paper, four types of classifiers based on Naïve Bayes, SVM, MLP neural network, and decision tree (j48) are used to classify the PD dataset and the performances of these classifiers are examined when they are implemented upon the actual PD dataset, discretized PD dataset, and selected set of attributes from PD dataset. The dataset used in this study comprises a range of voice signals from 31 people: 23 with PD and 8 healthy people. The result shows that Naïve Bayes and decision tree (j48) yield better accuracy when performed upon the discretized PD dataset with cross-validation test mode without applying any attributes selection algorithms. SVM gives high accuracy of 70% for training and 30% for the test when implemented on a discretized PD dataset and a splitting dataset. The MLP neural network gives the highest accuracy when used to classify actual PD dataset without discretization, attribute selection, or changing test mode.

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Early Detection of Parkinson’s Disease as a Pre-diagnosis Tool Using Various Classification Techniques on Vocal Features

Behera

Bal

Sarraf

2022

Lecture Notes in Computer Science

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Application of neural networks in early detection and diagnosis of Parkinson's disease

Cited by 25 publications

References 6 publications

Heterogeneous Acceleration of Hybrid PSO-QN Algorithm for Neural Network Training

Heterogeneous Acceleration of Hybrid PSO-QN Algorithm for Neural Network Training

Parkinson’s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson’s Disease Dataset on the Performance of Classifier Algorithms

Early Detection of Parkinson’s Disease as a Pre-diagnosis Tool Using Various Classification Techniques on Vocal Features

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