Malaria Severity Classiffication through Jordan-Elman Neural Network Based on Features Extracted from Thick Blood Smear

Title:

Malaria Severity Classiffication through Jordan-Elman Neural Network Based on Features Extracted from Thick Blood Smear

Creator:

Chiroma, H., Abdul-kareem , S., Ibrahim , U., Gadam Ahmad, I., Garba, A., Abubakar , A. , Fatihu Hamza , M., and Herawan , T.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:24cb1627-f9b5-4222-9b9c-38865a047103
uuid:24cb1627-f9b5-4222-9b9c-38865a047103
doi:10.14311/NNW.2015.25.028

Subject:

learning algorithms, Jordan-Elman neural network, malaria, model performance, and evaluation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This article presents an alternative approach useful for medical practitioners who wish to detect malaria and accurately identify the level of severity. Malaria classi?ers are usually based on feed forward neural networks. In this study, the proposed classiffier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classiffier 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 classiffier 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

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:5

Harvested from:

CDK

Metadata only:

false