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Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis

Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based...

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Main Authors:	Majeed Alneamy, Jamal Salahaldeen, Hameed Alnaish, Zakaria A., Mohd. Hashim, S. Z., Hamed Alnaish, Rahma A.
Format:	Article
Published:	Elsevier Ltd 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/89578/ http://dx.doi.org/10.1016/j.compbiomed.2019.103348
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id	my.utm.89578
record_format	eprints
spelling	my.utm.895782021-02-22T05:55:56Z http://eprints.utm.my/id/eprint/89578/ Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis Majeed Alneamy, Jamal Salahaldeen Hameed Alnaish, Zakaria A. Mohd. Hashim, S. Z. Hamed Alnaish, Rahma A. QA75 Electronic computers. Computer science Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies. Elsevier Ltd 2019-09 Article PeerReviewed Majeed Alneamy, Jamal Salahaldeen and Hameed Alnaish, Zakaria A. and Mohd. Hashim, S. Z. and Hamed Alnaish, Rahma A. (2019) Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis. Computers in Biology and Medicine, 112 . p. 103348. ISSN 0010-4825 http://dx.doi.org/10.1016/j.compbiomed.2019.103348
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Majeed Alneamy, Jamal Salahaldeen Hameed Alnaish, Zakaria A. Mohd. Hashim, S. Z. Hamed Alnaish, Rahma A. Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
description	Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies.
format	Article
author	Majeed Alneamy, Jamal Salahaldeen Hameed Alnaish, Zakaria A. Mohd. Hashim, S. Z. Hamed Alnaish, Rahma A.
author_facet	Majeed Alneamy, Jamal Salahaldeen Hameed Alnaish, Zakaria A. Mohd. Hashim, S. Z. Hamed Alnaish, Rahma A.
author_sort	Majeed Alneamy, Jamal Salahaldeen
title	Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
title_short	Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
title_full	Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
title_fullStr	Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
title_full_unstemmed	Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
title_sort	utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
publisher	Elsevier Ltd
publishDate	2019
url	http://eprints.utm.my/id/eprint/89578/ http://dx.doi.org/10.1016/j.compbiomed.2019.103348
_version_	1692991799535075328
score	13.211869

Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis

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