Assessment of Parkinson's Disease Progression Using Neural Network and Anfis Models

Hlavica, Jakub; Prauzek, Michal; Peterek, Tomáš; Musílek, Petr

doi:10.14311/nnw.2016.26.006

Cited by 13 publications

(8 citation statements)

References 21 publications

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“… 17 NN represents a broad class of computational models inspired by biological networks found in the central nervous systems and animal brains. They can be used to approximate unknown mapping of a large number of inputs, 12 such as the parameters derived from the wearable system used in this study. Moreover, the C-means clustering used to develop the ANFIS model improved the predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, regression trees can be studied to develop a prediction model able to overcome the discrete scores and severity levels of the traditional clinical scales toward the possibility to associate a continuous response score to each patient according to the pathology progression. In particular, supervised machine learning techniques, such as support vector regression (SVR), 17 random forest (RF), 3 adaptive neuro-fuzzy inference system (ANFIS), 12 , 17 and linear regression (LR) 11 were tested in this work. Based on preliminary investigations with different hyperparameters configurations, default hyperparameters tuning was used since it allowed obtaining the best accuracy in regression models.…”

Section: Methodsmentioning

confidence: 99%

“… 22 In this study, we aimed at investigating the relationship between the extracted information from the sensors and the clinical scores provided by the clinicians according to the MDS-UPDRS. To achieve this goal, multiple feature selection methods, 4 , 15 combined with multiple regression models, 11 , 12 , 17 , 25 were investigated to understand which technique could be the most helpful for longitudinal clinical data analysis and long-term monitoring of PD progression. We already investigated classification approach with supervised learning in previous works, considering both binary classification 21 (i.e., PD patients vs healthy controls), and multigroup classification (i.e., PD patients, healthy subjects, and subjects with idiopathic hyposmia that are at risk for developing the disease).…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

et al. 2020

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Parkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

et al. 2020

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“…These systems are capable of modeling the nonlinear relation between input and output of a system [19]. In the literature, there are different ANFIS applications in areas such as environmental engineering [37], health informatics [7], earth sciences [20], agricultural & biosystems engineering [33], synthesis of production processes [15]. The most important criterion regarding ANFIS structure is to be tuning its antecedent and consequent parameters, this procedure is known as the training phase.…”

Section: Introductionmentioning

confidence: 99%

Improved Antlion Optimizer Algorithm and Its Performance on Neuro Fuzzy Inference System

Kilic¹,

Yüzgeç²,

Karakuzu³

2019

NNW

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Antlion optimizer algorithm (ALO) is inspired by hunting strategy of antlions. In this study, an improved antlion optimization algorithm is proposed for training parameters of adaptive neuro fuzzy inference system (ANFIS). In the standard ALO algorithm, the greatest deficiency is its long running time during optimization process. The random walking model of ants, the selection procedure and boundary checking mechanism have been developed to speed up standard ALO algorithm. To evaluate the performance of the improved antlion optimization algorithm (IALO), it has been tested on dynamic system modelling problems. ANFIS's parameters has been optimized by IALO algorithm to model five dynamic systems. ANFIS training procedure has been performed with 30 independent runs. Each training has been started with the random initial parameters of ANFIS and performance metrics have been obtained at the end of training. The results show that the IALO algorithm is able to provide competitive results in terms of mean, best, worst, standard deviation, training time metrics. According to the training time result, the proposed IALO algorithm has better performance than standard ALO algorithm and the average training time has been reduced to approximately 80 %.

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“…There are several other examples of research that are applying the inspiration by brain structures into the field of artificial intelligence, such as grey wolf optimization [4], genetic feature selector [5], associative memory [6] or fuzzy neural network [7]. In many applications, the biological motivation for the paradigm of neural networks is an advantage because the modeled processes are also of biological origin, such as automated analysis of medical or physiological data [8,9] or mental processes modeling [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Homeostatic Learning Rule for Artificial Neural Networks

Ruzek¹

2018

NNW

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This article presents an improvement of learning algorithm for an artificial neural network that makes the learning process more similar to a biological neuron, but still simple enough to be easily programmed. This idea is based on autonomous artificial neurons that are working together and at same time competing for resources; every neuron is trying to be better than the others, but also needs the feed back from other neurons. The proposed artificial neuron has similar forward signal processing as the standard perceptron; the main difference is the learning phase. The learning process is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in a forward direction into the higher layer, while the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how the neuron's output is accepted by the others. The neurons are trying to find such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept-the learning process is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization.

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Assessment of Parkinson's Disease Progression Using Neural Network and Anfis Models

Cited by 13 publications

References 21 publications

Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

Improved Antlion Optimizer Algorithm and Its Performance on Neuro Fuzzy Inference System

Homeostatic Learning Rule for Artificial Neural Networks

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