A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Authors:
Seral Özşen;Sadık Kara;Fatma Latifoğlu;Salih Güneş
Affiliations:
Selcuk University, Department of Electrical & Electronics Engineering, 42075 Konya, Turkey;Erciyes University, Department of Electronics Engineering, 38039 Kayseri, Turkey;Erciyes University, Department of Electronics Engineering, 38039 Kayseri, Turkey;Selcuk University, Department of Electrical & Electronics Engineering, 42075 Konya, Turkey
Venue:
Computer Methods and Programs in Biomedicine
Year:
2007

Citing 7
Cited 4

Methodologies from machine learning in data analysis and software

The Computer Journal - Special issue on distributed systems
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

Computers in Biology and Medicine
Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

Computers in Biology and Medicine
Diagnosis of atherosclerosis from carotid artery Doppler signals as a real-world medical application of artificial immune systems

Expert Systems with Applications: An International Journal
Medical application of Artificial Immune Recognition System (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals

Computers in Biology and Medicine
MILA: multilevel immune learning algorithm

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI

Use of kernel functions in artificial immune systems for the nonlinear classification problems

IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model

Expert Systems with Applications: An International Journal
Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

Computer Methods and Programs in Biomedicine
Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.