A parallel neural network approach to prediction of Parkinson’s Disease

ström, Freddie; Köker, Raşit

doi:10.1016/j.eswa.2011.04.028

Cited by 125 publications

(53 citation statements)

References 14 publications

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“…Adding more experts to the committee with propagation of unlearned data to the next expert may improve prediction accuracy as it is proven in [25] for a different domain. Additionally, focusing on the process of features selection would be also relatively helpful to recognize more informative features among many others by employing principle component analysis on raw data consisting of high number of attributes.…”

Section: Resultsmentioning

confidence: 99%

Authorship attribution using committee machines with k-nearest neighbors rated voting

Kuşakçı

2012

11th Symposium on Neural Network Applications in Electrical Engineering

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Authorship attribution, namely determination of the author of a text, may become an extraordinarily complex and sensitive job due to its relatively difficult feature extraction phase and highly nonlinear nature. This paper proposes a classification tool using committee machines consisting of multilayered perceptron neural networks (MLP) to identify the author of a text. Each expert is an individual MLP learning complex inputoutput relation composed of 14 lexical, stylometric attributes extracted from the corpus. The resulting mapping after training is used to identify the texts in German language written by two different authors. Unlike other committee based classification tools individual answers of the experts are combined with a novel voting method, k-nearest neighbors rated voting. The proposed method shows very promising results when benchmarked with simple majority voting technique.

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Section: Resultsmentioning

confidence: 99%

Authorship attribution using committee machines with k-nearest neighbors rated voting

Kuşakçı

2012

11th Symposium on Neural Network Applications in Electrical Engineering

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“…In their study, support vector machine (SVM) in combination with the feature selection approach was taken to diagnose PD, the simulation results has shown that the proposed method can discriminate PD patients from healthy ones with approximately 90% classification accuracy using only four dysphonic features. After then, various techniques have been devel-oped to study the PD diagnosis problem from the perspective of dysphonic indicators, including Artificial Neural Networks (ANNs) [6,7], SVM [8,9], Dirichlet process mixtures [10], multi-kernel relevance vector machines [11], similarity classifier [12], rotation forest [13], fuzzy k-nearest neighbor (FKNN) [14]. Among the proposed methods, SVM has shown to be a very promising tool for diagnosing PD.…”

Section: Introductionmentioning

confidence: 99%

Fruit Fly Optimization Algorithm Based SVM Classifier for Efficient Detection of Parkinson’s Disease

Shen

Chen

Kang

et al. 2015

Advances in Swarm and Computational Intelligence

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Abstract. In this paper, we present a fruit fly optimization algorithm (FOA) based support vector machine (SVM) classification scheme, termed as FOA-SVM, and it is applied successfully to Parkinson's disease (PD) diagnosis. In the proposed FOA-SVM, the set of parameters in SVM is tackled efficiently by the FOA technique. The effectiveness and efficiency of FOA-SVM has been rigorously evaluated against the PD dataset by comparing with the particle swarm optimization algorithm (PSO) optimized SVM (PSO-SVM), and grid search technique based SVM (Grid-SVM). The experimental results demonstrate that the proposed approach outperforms the other two counterparts in terms of diagnosis accuracy as well as the fewer CPU time. Promisingly, the proposed method can be regarded as a useful clinical decision tool for the physicians.

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“…Among neural network, logistic regression, decision tree and data mining approaches neural network with Levenberg -Marquardt algorithm showed the best performance. Strom et al [27] tried parallel neural networks for PD classification. Sateesh Badu et.…”

Section: Introductionmentioning

confidence: 99%

Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

Sachnev

Kim²

2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.

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A parallel neural network approach to prediction of Parkinson’s Disease

Cited by 125 publications

References 14 publications

Authorship attribution using committee machines with k-nearest neighbors rated voting

Authorship attribution using committee machines with k-nearest neighbors rated voting

Fruit Fly Optimization Algorithm Based SVM Classifier for Efficient Detection of Parkinson’s Disease

Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

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