An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach

Chen, Huiling; Huang, Changcheng; Yu, Xianjun; Xu, Xin; Sun, Xin; Wang, Gang; Wang, Sujing

doi:10.1016/j.eswa.2012.07.014

Cited by 259 publications

(123 citation statements)

References 28 publications

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“…When genetic algorithm with KNN classification method is applied 98.2% performance is obtained [9].The selected features by ANFC which were the combination of linear and non linear features are more powerful for classification than genetic algorithm [10]. The fuzzy Knearest neighbor (FKNN) with principal component analysis (PCA) to construct the most discriminative new feature set is used for PD diagnosis [11]. In this paper extended database is used with more features.…”

Section: Introductionmentioning

confidence: 99%

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Sharma¹,

Gupta²

2015

IJISA

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Abstract-Parkinson is a neurological disease and occurs due to lack of dopamine neurons. These dopamine neurons manage all body movements. Parkinson patients have difficulty in doing all daily routine activities, and also have disturbed vocal fold movements. Using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. In this paper, we have used 23 features dataset, all the features are analyzed and 15 features are selected from the total dataset. As in Parkinson tremor is present in the voice box muscles, so the variation in the period and amplitude of consecutive vocal cycles is present. The feature dataset selected consist of jitter, shimmer, harmonic to noise ratio, DFA, spread1 and PPE. Various classifiers are used and their comparison is done to find out which classifier is perfect in this environment. It is concluded that support vector classifiers as the best one with an accuracy of 96%. In the neural network classifiers with different transfer functions, there is tradeoff among the performance parameters.

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Section: Introductionmentioning

confidence: 99%

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Sharma¹,

Gupta²

2015

IJISA

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“…Using the upper and lower boundaries for each a-cut, the membership function MTBF can be obtained. The highest membership function is one of the conventional methods used in most previous studies (Lee et al 2001;Chen et al 2013;D'Urso et al 2017). In this paper, the highest membership value method is applied to defuzzify the fuzzy numbers through the following equation.…”

Section: Assumptionsmentioning

confidence: 99%

MTBF evaluation for 2-out-of-3 redundant repairable systems with common cause and cascade failures considering fuzzy rates for failures and repair: a case study of a centrifugal water pumping system

2017

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In many cases, redundant systems are beset by both independent and dependent failures. Ignoring dependent variables in MTBF evaluation of redundant systems hastens the occurrence of failure, causing it to take place before the expected time, hence decreasing safety and creating irreversible damages. Common cause failure (CCF) and cascading failure are two varieties of dependent failures, both leading to a considerable decrease in the MTBF of redundant systems. In this paper, the alpha-factor model and the capacity flow model are combined so as to incorporate CCF and cascading failure in the evaluation of MTBF of a 2-out-of-3 repairable redundant system. Then, using a transposed matrix, the MTBF function of the system is determined. Due to the fact that it is difficult to estimate the independent and dependent failure rates, industries are interested in considering uncertain failure rates. Therefore, fuzzy theory is used to incorporate uncertainty into the model presented in this study, and a nonlinear programming model is used to determine system's MTBF. Finally, in order to validate the proposed model, evaluation of MTBF of the redundant system of a centrifugal water pumping system is presented as a practical example.

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“…In [14] the author use PCA data reduction method to reduce the features of Parkinson's disease and the FKNN classifier is used for classification. The wrapper based improved naïve Bayes algorithm called RSNB is use to select the features randomly and is applied on large UCI datasets which gives the better classification accuracy [15].…”

Section: Existing Work 71 Feature Selection Algorithms On Medical Dmentioning

confidence: 99%

Analysis of Feature Selection Algorithms on Classification: A Survey

Vanaja¹,

Kumar²

2014

IJCA

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The aim of this paper is to discuss about various feature selection algorithms applied on different datasets to select the relevant features to classify data into binary and multi class in order to improve the accuracy of the classifier. Recent researches in medical diagnose uses the different kind of classification algorithms to diagnose the disease. For predicting the disease, the classification algorithm produces the result as binary class. When there is a multiclass dataset, the classification algorithm reduces the dataset into a binary class for simplification purpose by using any one of the data reduction methods and the algorithm is applied for prediction. When data reduction on original dataset is carried out, the quality of the data may degrade and the accuracy of an algorithm will get affected. To maintain the effectiveness of the data, the multiclass data must be treated with its original form without maximum reduction, and the algorithm can be applied on the dataset for producing maximum accuracy. Dataset with maximum number of attributes like thousands must incorporate the best feature selection algorithm for selecting the relevant features to reduce the space and time complexity. The performance of Classification algorithm is estimated by how accurately it predicts the individual class on particular dataset. The accuracy constrain mainly depends on the selection of appropriate features from the original dataset. The feature selection algorithms play an important role in classification for better performance. The feature selection is one of the preprocessing techniques in the classification. This research paper deals with different feature selection algorithms and their performance on different dataset. General TermsFeature selection algorithm, Medical dataset, Accuracy, Filter method, Wrapper method, embedded method.

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An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach

Cited by 259 publications

References 28 publications

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

MTBF evaluation for 2-out-of-3 redundant repairable systems with common cause and cascade failures considering fuzzy rates for failures and repair: a case study of a centrifugal water pumping system

Analysis of Feature Selection Algorithms on Classification: A Survey

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