Data Mining Classification Techniques and Performances on Medical Data

This study evaluates the performance of classification techniques with the application of several software, among them are Rosetta, Tanagra, Weka and Orange. The classification technique has been tested on six medical datasets from the UCI Machine Learning Repository. The study will help researcher...

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Main Author:	Benyehmad, Yahyia Mohammed M. Ali
Format:	Thesis
Language:	eng eng
Published:	2006
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/1864/1/Yahyia_Mohammed_M._Ali_Benyehmad_-_Data_mining_classification_techniques_and_performances_on_medical_data.pdf https://etd.uum.edu.my/1864/2/Yahyia_Mohammed_M._Ali_Benyehmad_-_Data_mining_classification_techniques_and_performances_on_medical_data.pdf
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id	my-uum-etd.1864
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA76 Computer software
spellingShingle	QA76 Computer software Benyehmad, Yahyia Mohammed M. Ali Data Mining Classification Techniques and Performances on Medical Data
description	This study evaluates the performance of classification techniques with the application of several software, among them are Rosetta, Tanagra, Weka and Orange. The classification technique has been tested on six medical datasets from the UCI Machine Learning Repository. The study will help researchers to select the best suitable technique of classification problem for medical datasets in term of classification accuracy. In this thesis, sixteen classification techniques have been evaluated and compared. These are Radial Basis Function (RBF), Multilayer Perceptron (MLP) Neural Networks, Multi Linear Regression (MLR), Logistic Regression (LR), Classification Tree (ID3, C4.5, 548, CART), Naive Bayes (NB), Support Vector Machines (SVM), k- Nearest Neighbors (kNN), Linear discriminate analysis (LDA),Rule based classifier, Standard voting, Voting with object tracking and Standard tuned voting (RSES). The experiments have been validated using 10-fold cross validation method. The results of the study shows that the most suitable classification technique is NB with an average classification accuracy of 90.13% and an average error rate of 9.87%. The worst classification technique is SLR with an average classification accuracy of 50.16% and an average error rate of 49.84%. The classification techniques has been ranked from the best to the worst based on average classification accuracy and average error rate. The top of the rank is NB and the bottom is SLR. The sequence of ranking from the best to the worst is NB, LDA, LR, SVM, C4.5, MLP, RBF, kNN, RuleB, ID3, CART, 548, SV, RSES, V, and SLR.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Benyehmad, Yahyia Mohammed M. Ali
author_facet	Benyehmad, Yahyia Mohammed M. Ali
author_sort	Benyehmad, Yahyia Mohammed M. Ali
title	Data Mining Classification Techniques and Performances on Medical Data
title_short	Data Mining Classification Techniques and Performances on Medical Data
title_full	Data Mining Classification Techniques and Performances on Medical Data
title_fullStr	Data Mining Classification Techniques and Performances on Medical Data
title_full_unstemmed	Data Mining Classification Techniques and Performances on Medical Data
title_sort	data mining classification techniques and performances on medical data
granting_institution	Universiti Utara Malaysia
granting_department	Faculty of Information Technology
publishDate	2006
url	https://etd.uum.edu.my/1864/1/Yahyia_Mohammed_M._Ali_Benyehmad_-_Data_mining_classification_techniques_and_performances_on_medical_data.pdf https://etd.uum.edu.my/1864/2/Yahyia_Mohammed_M._Ali_Benyehmad_-_Data_mining_classification_techniques_and_performances_on_medical_data.pdf
_version_	1747827220896808960
spelling	my-uum-etd.18642013-07-24T12:13:28Z Data Mining Classification Techniques and Performances on Medical Data 2006 Benyehmad, Yahyia Mohammed M. Ali Faculty of Information Technology Faculty of Information Technology QA76 Computer software This study evaluates the performance of classification techniques with the application of several software, among them are Rosetta, Tanagra, Weka and Orange. The classification technique has been tested on six medical datasets from the UCI Machine Learning Repository. The study will help researchers to select the best suitable technique of classification problem for medical datasets in term of classification accuracy. In this thesis, sixteen classification techniques have been evaluated and compared. These are Radial Basis Function (RBF), Multilayer Perceptron (MLP) Neural Networks, Multi Linear Regression (MLR), Logistic Regression (LR), Classification Tree (ID3, C4.5, 548, CART), Naive Bayes (NB), Support Vector Machines (SVM), k- Nearest Neighbors (kNN), Linear discriminate analysis (LDA),Rule based classifier, Standard voting, Voting with object tracking and Standard tuned voting (RSES). The experiments have been validated using 10-fold cross validation method. The results of the study shows that the most suitable classification technique is NB with an average classification accuracy of 90.13% and an average error rate of 9.87%. The worst classification technique is SLR with an average classification accuracy of 50.16% and an average error rate of 49.84%. The classification techniques has been ranked from the best to the worst based on average classification accuracy and average error rate. The top of the rank is NB and the bottom is SLR. The sequence of ranking from the best to the worst is NB, LDA, LR, SVM, C4.5, MLP, RBF, kNN, RuleB, ID3, CART, 548, SV, RSES, V, and SLR. 2006 Thesis https://etd.uum.edu.my/1864/ https://etd.uum.edu.my/1864/1/Yahyia_Mohammed_M._Ali_Benyehmad_-_Data_mining_classification_techniques_and_performances_on_medical_data.pdf application/pdf eng validuser https://etd.uum.edu.my/1864/2/Yahyia_Mohammed_M._Ali_Benyehmad_-_Data_mining_classification_techniques_and_performances_on_medical_data.pdf application/pdf eng public masters masters Universiti Utara Malaysia Antonie, M.-L., Za'yane, O.R. and Coman, A. (2001)." Application of Data Mining Techniques for Medical Image Classification". Proceeding of the Second International Workshop on Multimedia Data Mining (MDM/KDD).San Francisco, USA, August. Apte, C., and Hong, S. J. 1996. Predicting Equity Returns from Securities Data with Minimal Rule Generation. In Advances in Knowledge Discovery and Data Mining, eds. U. Fayyad, G. Piatetsky-Shapiro, P. 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Data Mining Classification Techniques and Performances on Medical Data

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