NDLI: Performance evaluation of combined feature selection and classification methods in diagnosing parkinson disease based on voice feature

Content Provider	IEEE Xplore Digital Library
Author	Wibawa, Made Satria Nugroho, Hanung Adi Setiawan, Noor Akhmad
Copyright Year	2015
Description	Author affiliation: Department of Electrical Engineering and Information Technology Faculty of Engineering, Universitas Gadjah Mada, Jln. Grafika 2 Yogyakarta 55281 Indonesia (Wibawa, Made Satria; Nugroho, Hanung Adi; Setiawan, Noor Akhmad)
Abstract	Parkinson is a disease attacking the nervous system and worsens the work of nervous system over time. This disease is incurable, the therapy existing today is only able to help to relieve the symptoms. Hence, an early diagnose is deemed essential to determine an accurate type of therapy. Parkinson disease can be diagnosed by examining the symptoms apparent to the patient. One of the symptoms is the existence of dysphonia (weakness in voice production) to the patients with Parkinson. This research purposely is to examine the diagnosis of Parkinson disease through the measurement of voice data obtained from UCI repository. The dataset of the voice was initially normalized before conducting the feature selection by means of a number of methods including Correlation-based Feature Selection (CFS), Principal Component Analysis (PCA), Wrapper and conducting without feature selection. The data that has been selected later was classified using four classifiers including Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Bayesian Network and Multi Layer Perceptron (MLP). All of the processes conducted using WEKA 3.6. The result revealed that the highest accuracy was obtained at 98.97% with the sensitivity of 99.32% and specificity of 97.92% from the use of feature selection of Wrapper using kNN classifiers.
Starting Page	126
Ending Page	131
File Size	248552
Page Count	6
File Format	PDF
ISBN	9781479983841
e-ISBN	9781479983865
DOI	10.1109/ICSITech.2015.7407790
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-27
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrapper Correlation KNN SVM Data mining WEKA Information technology Parkinson Diseases PCA Support vector machines MLP CFS Bayes methods Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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