Artificial Neural Networks and Support Vector Machines for Parkinson Disease Detection using Human Voice
نویسندگان
چکیده
Artificial neural network(ANN) with tansig, logsig and purelin transfer function, support vector machines(SVM), linear and quadratic classifiers are used in this work for the detection of Parkinson disease using voice features. In the Parkinson disease, voice of a person changes because of presence of tremor in the voicebox muscles. Total 195 phonations were used for the analysis from twenty three Parkinsonand eight healthy subjects. For the classification purpose fifteen features dataset is prepared. Parkinson voice samples are differentiated from the healthy voice samples with 96% accuracy. Key-Words: -Voice, ANN, SVM, Parkinson Disease (PD), Feature extraction, classification
منابع مشابه
Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data
This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values. Seismic surveying was performed next on these models. F...
متن کاملVoice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines
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 ...
متن کاملA Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models
متن کامل
Intrusion Detection: Support Vector Machines and Neural Networks
This paper concerns intrusion detection and audit trail reduction. We describe approaches to intrusion detection and audit data reduction using support vector machines and neural networks. Using a set of benchmark data from the KDD (Knowledge Discovery and Data Mining) competition designed by DARPA, we demonstrate that efficient and highly accurate classifiers can be built using either support ...
متن کاملProbabilistic Contaminant Source Identification in Water Distribution Infrastructure Systems
Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology...
متن کامل