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Performance Evaluation of Neural Classifiers Through Confusion Matrices To Diagnose Skin Conditions
Ihsan Omur BUCAK
Pages - 15 - 27     |    Revised - 24-02-2014     |    Published - 19-03-2014
Volume - 5   Issue - 2    |    Publication Date - March 2014  Table of Contents
Artificial Intelligence, Artificial Neural Networks, Medical Diagnosing, Neural Classifiers, Skin Conditions/Diseases, Confusion Matrix, F-score.
In this paper we have aimed to diagnose skin conditions using Artificial Intelligence (AI) based classifier algorithms and do the performance analyses of those presented algorithms through confusion matrices. These algorithms are being used in a large array of different areas including medicine, and display very distinct characteristics in the sense that they are grouped under different categories such as supervised, unsupervised, statistical, or optimization. The objective of this study is to diagnose skin conditions using seven different well-known and popular as well as emerging Artificial Intelligence based algorithms and to help general practitioners and/or dermatologists develop a careful and supportive approach that leads to a probable diagnosis of skin conditions or diseases. These algorithms we chose as neural classifiers include Back- Propagation (BP), Random Forest (RF), Support Vector Machines (SVMs), Linear Vector Quantization (LVQ), Self-Organizing Maps (SOMs), Naïve Bayes, and finally Bayesian Networks. All of these algorithms have been tested and their results of diagnosing skin conditions/diseases by using data set from Dermatology Database have been compared.
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Associate Professor Ihsan Omur BUCAK
Melikºah University - Turkey

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