Performance of Classification Techniques on Medical Datasets

KEMAL, TUTUNCU and MURAT, KOKLU (2015) Performance of Classification Techniques on Medical Datasets. In: Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015, 10-11 December, 2015, Rome, Italy.

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Abstract

The definition of the data mining can be told as to extract information or knowledge from large volumes of data. One of the main challenging area of data mining is classification. There are so many different classification algorithm in literature ranging from statistical based to artificial intelligence based. This study make use of Waikato Environment for Knowledge Analysis or in short, WEKA to compare the different classification techniques on different medical datasets. 23 different classification techniques were applied to three different medical datasets namely EEG Eye State, Fertility and Thoracic Surgery Medical Datasets that were taken from UCI Machine Learning Repository. The results showed that Multilayer Perceptron (MLP) had highest accuracy for Fertility Dataset (90%), three different techniques namely Bagging, Dagging and Grading had highest and same accuracies for Thoracic Surgery Data Set (85.1064%) and finally Kstar had highest accuracy for EEG Eye State Dataset (96.7757%).

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data Mining, Multilayer Perceptron, Grading, Kstar, Classification, EEG Eye State Dataset, Thoracic Surgery Data Set, Fertility Dataset
Depositing User: Mr. John Steve
Date Deposited: 04 Apr 2019 11:58
Last Modified: 04 Apr 2019 11:58
URI: http://publications.theired.org/id/eprint/1145

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