Optimization of neural network using cuckoo search for the classification of diabetes

Available records show that over 80% of the patient suffering from diabetes die from heart or blood diseases. Total cure for the diabetes is currently not available. In this paper, we proposed diabetes classifier based on the cuckoo search algorithm (CS) and Neural Network (NN). The weights and bias...

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Main Authors:	Abubakar, Adamu, Shuib, Liyana, Chiroma, Haruna
Format:	Article
Language:	English English
Published:	American Scientific Publishers 2015
Subjects:	Q350 Information theory
Online Access:	http://irep.iium.edu.my/51917/ http://irep.iium.edu.my/51917/ http://irep.iium.edu.my/51917/ http://irep.iium.edu.my/51917/1/12CTN12-4713-NEWNewNew.pdf http://irep.iium.edu.my/51917/7/51917-Optimization%20of%20neural%20network%20using%20cuckoo%20search%20for%20the%20classification%20of%20diabetes_SCOPUS.pdf

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recordtype	eprints
spelling	iium-519172016-09-19T07:56:35Z http://irep.iium.edu.my/51917/ Optimization of neural network using cuckoo search for the classification of diabetes Abubakar, Adamu Shuib, Liyana Chiroma, Haruna Q350 Information theory Available records show that over 80% of the patient suffering from diabetes die from heart or blood diseases. Total cure for the diabetes is currently not available. In this paper, we proposed diabetes classifier based on the cuckoo search algorithm (CS) and Neural Network (NN). The weights and bias of the NN was trained using the CS to deviate from being stuck in local minima. The high dimension of the features in our dataset triggered the study to extract the critical features using principal component analysis. The extracted features were used to built a classifier based on the NN and the CS for classifying potential diabetes patients. The propose diabetes classifier performance was compared to the classifiers built based on artificial bee colony and genetic algorithm. Simulation results show that the proposed approach converges faster to the optimum solution than the comparative classifiers. Comparative study of the approach proposed and previous methods, further proved the effectiveness of our method. The classifier has provided promising classification result in the classifying of potential diabetic patients. The classifier have the capability of automatically diagnosing possible diabetic patients. This can be of help to the physicians in taken decision about the status of a diabetic patient American Scientific Publishers 2015-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51917/1/12CTN12-4713-NEWNewNew.pdf application/pdf en http://irep.iium.edu.my/51917/7/51917-Optimization%20of%20neural%20network%20using%20cuckoo%20search%20for%20the%20classification%20of%20diabetes_SCOPUS.pdf Abubakar, Adamu and Shuib, Liyana and Chiroma, Haruna (2015) Optimization of neural network using cuckoo search for the classification of diabetes. Journal of Computational and Theoretical Nanoscience, 12 (12). pp. 5755-5758. ISSN 1546-1955 E-ISSN 1546-1963 http://www.ingentaconnect.com/contentone/asp/jctn/2015/00000012/00000012/art00109 10.1166/jctn.2015.4713
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q350 Information theory
spellingShingle	Q350 Information theory Abubakar, Adamu Shuib, Liyana Chiroma, Haruna Optimization of neural network using cuckoo search for the classification of diabetes
description	Available records show that over 80% of the patient suffering from diabetes die from heart or blood diseases. Total cure for the diabetes is currently not available. In this paper, we proposed diabetes classifier based on the cuckoo search algorithm (CS) and Neural Network (NN). The weights and bias of the NN was trained using the CS to deviate from being stuck in local minima. The high dimension of the features in our dataset triggered the study to extract the critical features using principal component analysis. The extracted features were used to built a classifier based on the NN and the CS for classifying potential diabetes patients. The propose diabetes classifier performance was compared to the classifiers built based on artificial bee colony and genetic algorithm. Simulation results show that the proposed approach converges faster to the optimum solution than the comparative classifiers. Comparative study of the approach proposed and previous methods, further proved the effectiveness of our method. The classifier has provided promising classification result in the classifying of potential diabetic patients. The classifier have the capability of automatically diagnosing possible diabetic patients. This can be of help to the physicians in taken decision about the status of a diabetic patient
format	Article
author	Abubakar, Adamu Shuib, Liyana Chiroma, Haruna
author_facet	Abubakar, Adamu Shuib, Liyana Chiroma, Haruna
author_sort	Abubakar, Adamu
title	Optimization of neural network using cuckoo search for the classification of diabetes
title_short	Optimization of neural network using cuckoo search for the classification of diabetes
title_full	Optimization of neural network using cuckoo search for the classification of diabetes
title_fullStr	Optimization of neural network using cuckoo search for the classification of diabetes
title_full_unstemmed	Optimization of neural network using cuckoo search for the classification of diabetes
title_sort	optimization of neural network using cuckoo search for the classification of diabetes
publisher	American Scientific Publishers
publishDate	2015
url	http://irep.iium.edu.my/51917/ http://irep.iium.edu.my/51917/ http://irep.iium.edu.my/51917/ http://irep.iium.edu.my/51917/1/12CTN12-4713-NEWNewNew.pdf http://irep.iium.edu.my/51917/7/51917-Optimization%20of%20neural%20network%20using%20cuckoo%20search%20for%20the%20classification%20of%20diabetes_SCOPUS.pdf
first_indexed	2023-09-18T21:13:36Z
last_indexed	2023-09-18T21:13:36Z
_version_	1777411415864246272

Optimization of neural network using cuckoo search for the classification of diabetes

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