Abstract
This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.
Highlights
Pattern recognition and data mining are the techniques that allow for the acquirement of meaningful information from large-scale data using a computer program
The developed approach has been tested for the diagnosis of liver diseases and diabetes, which are commonly seen in the society and both reduce the quality of life
ABCFS + support vector machine (SVM) method test results developed for the hepatitis dataset, liver disorders dataset and diabetes datasets are given in Pseudocode 3
Summary
Pattern recognition and data mining are the techniques that allow for the acquirement of meaningful information from large-scale data using a computer program. These techniques are extensively used, in the military, medical, and industrial application fields, since there is a continuously increasing amount and type of data in these areas, due to advanced data acquisition systems For this reason, for the obtained data set, data reduction algorithms are needed for filtering, priority sorting, and providing redundant measurements to detect the feature selection. An automatic diagnosis system using linear discriminant analysis (LDA) and adaptive network based on fuzzy inference system (ANFIS) was proposed by Dogantekin et al [5] for hepatitis diseases This automatic diagnosis system of hepatitis disease diagnostics was obtained with a classification accuracy of about 94.16%. Bascil and Temurtas [6] realized a hepatitis disease diagnosis based on a multilayer neural network structure that used the Levenberg- Marquardt algorithm as training algorithm for the weights update with a classification accuracy of 91.87% from 10-fold cross-validation
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