Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis

Manual medical diagnosis which depends on physicians’ knowledge to diagnose the presence of the symptoms of the disease is impracticable. Therefore, automatic and intelligent medical diagnosis has become very useful to the physicians when dealing with huge amount and high dimensional medical databas...

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Main Authors: Hordri, N. F., Yuhaniz, S. S., Shamsuddin, S. M., Ali, A.
Format: Article
Published: American Scientific Publishers 2017
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Online Access:http://eprints.utm.my/id/eprint/75297/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027858595&doi=10.1166%2fasl.2017.7364&partnerID=40&md5=1a90bdf7cdf8ee5db4cd51d4a54a6056
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spelling my.utm.752972018-03-27T06:10:47Z http://eprints.utm.my/id/eprint/75297/ Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis Hordri, N. F. Yuhaniz, S. S. Shamsuddin, S. M. Ali, A. TP Chemical technology Manual medical diagnosis which depends on physicians’ knowledge to diagnose the presence of the symptoms of the disease is impracticable. Therefore, automatic and intelligent medical diagnosis has become very useful to the physicians when dealing with huge amount and high dimensional medical database. In this paper, we have proposed hybridization method by improving MLP learning with Biogeography Based Optimization (BBO) to be adopted and applied in five medical diagnoses. Comparisons are done between the following proposed methods: hybrid Particle Swarm Optimization (PSO) and MLP; hybrid Genetic Algorithm (GA) and MLP; and hybrid Artificial Fish Swarm Algorithm (AFSA) and MLP using the same standard parameters. Results are analyzed in terms of their classification accuracy. The performance of each method was evaluated based on their specificity, sensitivity, accuracy and precision. The findings disclose that BBO is a promising optimization tool in enhancing MLP learning with better average accuracy and convergence rate in intelligent medical diagnosis. American Scientific Publishers 2017 Article PeerReviewed Hordri, N. F. and Yuhaniz, S. S. and Shamsuddin, S. M. and Ali, A. (2017) Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis. Advanced Science Letters, 23 (6). pp. 5304-5308. ISSN 1936-6612 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027858595&doi=10.1166%2fasl.2017.7364&partnerID=40&md5=1a90bdf7cdf8ee5db4cd51d4a54a6056 DOI:10.1166/asl.2017.7364
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 TP Chemical technology
spellingShingle TP Chemical technology
Hordri, N. F.
Yuhaniz, S. S.
Shamsuddin, S. M.
Ali, A.
Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
description Manual medical diagnosis which depends on physicians’ knowledge to diagnose the presence of the symptoms of the disease is impracticable. Therefore, automatic and intelligent medical diagnosis has become very useful to the physicians when dealing with huge amount and high dimensional medical database. In this paper, we have proposed hybridization method by improving MLP learning with Biogeography Based Optimization (BBO) to be adopted and applied in five medical diagnoses. Comparisons are done between the following proposed methods: hybrid Particle Swarm Optimization (PSO) and MLP; hybrid Genetic Algorithm (GA) and MLP; and hybrid Artificial Fish Swarm Algorithm (AFSA) and MLP using the same standard parameters. Results are analyzed in terms of their classification accuracy. The performance of each method was evaluated based on their specificity, sensitivity, accuracy and precision. The findings disclose that BBO is a promising optimization tool in enhancing MLP learning with better average accuracy and convergence rate in intelligent medical diagnosis.
format Article
author Hordri, N. F.
Yuhaniz, S. S.
Shamsuddin, S. M.
Ali, A.
author_facet Hordri, N. F.
Yuhaniz, S. S.
Shamsuddin, S. M.
Ali, A.
author_sort Hordri, N. F.
title Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
title_short Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
title_full Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
title_fullStr Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
title_full_unstemmed Hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
title_sort hybrid biogeography based optimization—multilayer perceptron for application in intelligent medical diagnosis
publisher American Scientific Publishers
publishDate 2017
url http://eprints.utm.my/id/eprint/75297/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027858595&doi=10.1166%2fasl.2017.7364&partnerID=40&md5=1a90bdf7cdf8ee5db4cd51d4a54a6056
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score 13.211869