Diagnosis of Parkinson’s disease using evolutionary algorithms

Smith, Stephen L.; Gaughan, P.T.; Halliday, David M.; Ju, Quan; Aly, Nabil; Playfer, Jeremy

doi:10.1007/s10710-007-9043-9

Cited by 26 publications

(9 citation statements)

References 10 publications

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“…Unlike our earlier diagnostic work [6,10], which used window-based GP classifiers, ABNs have access to both local (e.g. local patterns of acceleration) and global (e.g.…”

Section: Resultsmentioning

confidence: 99%

Evolving Computational Dynamical Systems to Recognise Abnormal Human Motor Function

Lones

Smith

Tyrrell

et al. 2012

Information Processign in Cells and Tissues

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Abstract. Artificial biochemical networks (ABNs) are a class of computational automata whose architectures are motivated by the organisation of genetic and metabolic networks. In this work, we investigate whether evolved ABNs can carry out classification when stimulated with time series data collected from human subjects with and without Parkinson's disease. The evolved ABNs have accuracies in the region of 80-90%, significantly higher than the diagnostic accuracies typically found in initial clinical diagnosis. We also show that relatively simple ABNs, comprising only a small number of discrete maps, are able to recognise the abnormal patterns of motor function associated with Parkinson's disease.

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“…Unlike our earlier diagnostic work [6,10], which used window-based GP classifiers, ABNs have access to both local (e.g. local patterns of acceleration) and global (e.g.…”

Section: Resultsmentioning

confidence: 99%

Evolving Computational Dynamical Systems to Recognise Abnormal Human Motor Function

Lones

Smith

Tyrrell

et al. 2012

Information Processign in Cells and Tissues

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“…In previous work on automated Parkinson's diagnosis [18], it was shown that evolved IRCGP solutions are able to describe acceleration patterns which are over-represented in the movements of Parkinson's patients relative to control subjects. In this work, we extend the approach to the multiclass cube drawing classification task described in Section II.…”

Section: Methodsmentioning

confidence: 99%

Implicit Context Representation Cartesian Genetic Programming for the assessment of visuo-spatial ability

Smith

Lones

2009

2009 IEEE Congress on Evolutionary Computation

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Abstract-In this paper, a revised form of Implicit Context Representation Cartesian Genetic Programming is used in the development of a diagnostic tool for the assessment of patients with neurological dysfunction such as Alzheimer's disease. Specifically, visuo-spatial ability is assessed by analysing subjects' digitised responses to a simple figure copying task using a conventional test environment. The algorithm was trained to distinguish between classes of visuo-spatial ability based on responses to the figure copying test by 7-11 year old children in which visuo-spatial ability is at varying stages of maturity. Results from receiver operating characteristic (ROC) analysis are presented for the training and subsequent testing of the algorithm and demonstrate this technique has the potential to form the basis of an objective assessment of visuo-spatial ability.

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“…Comparative analysis between the ANN, DT, Logistic Regression and data mining approaches on the PD classification proved the superiority of ANN with Levenberg-Marquardt algorithm to the others [14]. Genetic Programming (GP)-based PD classification was presented in [15] where PD diagnosis is done using analyzing simple gure copying tasks. GPbased feature selection on vocal features set with the classification scheme of Expectation Maximization (EM) was proposed in [16] for PD classification.…”

Section: Introductionmentioning

confidence: 99%

Efficient classification of Parkinson's disease using extreme learning machine and hybrid particle swarm optimization

Shahsavari

Rashidi

Bakhsh

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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One of the most well-known problems in machine learning framework is classification of Parkinson's Disease (PD) to patient people and healthy people. Due to the importance of that problem, utilization of a novel learning method is necessary. For this purpose, this paper proposes the utilization of Extreme Learning Machine (ELM) as a type of feed-forward neural network with a single hidden layer to classify the PD patients. However ELM is known as the one of the fast and accurate learning methods, selection of relevant feature elements of PD dataset can be effective on improving the classification performance of ELM. To this end, this paper proposes Hybrid Particle Swarm Optimization (PSO) as the second innovation to efficiently select the relevant feature elements. The main advantage of Hybrid PSO is locally improving of particles in order to jump over the local optimum solution and quickly converging to the global optimal solution. Evaluation of the proposed method on PD dataset proves the superiority of the propose method on the problem of PD classification, in comparison to the other learning methods.

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Diagnosis of Parkinson’s disease using evolutionary algorithms

Cited by 26 publications

References 10 publications

Evolving Computational Dynamical Systems to Recognise Abnormal Human Motor Function

Evolving Computational Dynamical Systems to Recognise Abnormal Human Motor Function

Implicit Context Representation Cartesian Genetic Programming for the assessment of visuo-spatial ability

Efficient classification of Parkinson's disease using extreme learning machine and hybrid particle swarm optimization

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