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Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear

Haruna, Chiroma and Abdul kareem, Sameem and Umar, Ibrahim and Ahmad, Gadam and Abdulmumini , Garba and Abubakar, Adamu and Fatihu, Mukhtar and Herawan, Tutut (2015) Malaria severity classification through Jordan-Elman neural network based on features extracted from thick blood smear. Neural Network World , 5 (15). pp. 565-584. ISSN 1210-0552

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Abstract

This article presents an alternative approach useful for medical prac- titioners who wish to detect malaria and accurately identify the level of severity. Malaria classifiers are usually based on feed forward neural networks. In this study, the proposed classifier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitiv- ity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classifier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classifier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent

Item Type: Article (Journal)
Additional Information: 7132/46647
Uncontrolled Keywords: learning algorithms, Jordan-Elman neural network, malaria, model performance, evaluation
Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Information and Communication Technology > Department of Information System
Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User: Dr Adamu Abubakar
Date Deposited: 30 Dec 2015 08:35
Last Modified: 16 Nov 2017 17:42
URI: http://irep.iium.edu.my/id/eprint/46647

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