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Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection

Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases predict...

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Main Authors: Jimoh, Rasheed Gbenga, Abisoye, Opeyemi Aderiike, Uthman, Muhammed Mubashir Babatunde
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2022
Subjects:
QA75 Electronic computers. Computer science
RA0421 Public health. Hygiene. Preventive Medicine
Online Access:https://repo.uum.edu.my/id/eprint/28755/1/JICT%2021%2001%202022%20117-148.pdf
https://doi.org/10.32890/jict2022.21.1.6
https://repo.uum.edu.my/id/eprint/28755/
https://e-journal.uum.edu.my/index.php/jict/article/view/10958
https://doi.org/10.32890/jict2022.21.1.6
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spelling my.uum.repo.287552023-02-08T01:28:53Z https://repo.uum.edu.my/id/eprint/28755/ Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection Jimoh, Rasheed Gbenga Abisoye, Opeyemi Aderiike Uthman, Muhammed Mubashir Babatunde QA75 Electronic computers. Computer science RA0421 Public health. Hygiene. Preventive Medicine Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases prediction, and proneness to local minimum errors in finding reliable testing output due to complexity of features in the feature space, which is a black box in nature. This study adopted a stacking method of heterogeneous ensemble learning of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict multiclass, symptomatic, and climatic malaria infection. ANN produced 48.33 percent accuracy, 60.61 percent sensitivity, and 45.58 percent specificity. SVM with Gaussian kernel function gave better performance results of 85.60 percent accuracy, 84.06 percent sensitivity, and 86.09 percent specificity. Consequently, to improve prediction performance, a stacking method was introduced to ensemble SVM with ANN. The proposed ensemble malaria model was tuned on different thresholds at a threshold value of 0.60, the ensemble model gave an optimum accuracy of 99.86 percent, sensitivity 100 percent, specificity 98.68 percent, and mean square error 0.14. The ensemble model experimental results indicated that stacked multiple classifiers produced better results than a single model. This research demonstrated the efficiency of heterogeneous stacking ensemble model on effects of climatic variations on multiclass malaria infection classification. Furthermore, the model reduced complexity, overfitting, low rate of convergence, and proneness to local minimum error problems of multiclass malaria infection in comparison to previous related models. Universiti Utara Malaysia Press 2022 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/28755/1/JICT%2021%2001%202022%20117-148.pdf Jimoh, Rasheed Gbenga and Abisoye, Opeyemi Aderiike and Uthman, Muhammed Mubashir Babatunde (2022) Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection. Journal of Information and Communication Technology, 21 (01). pp. 117-148. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/10958 https://doi.org/10.32890/jict2022.21.1.6 https://doi.org/10.32890/jict2022.21.1.6
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
RA0421 Public health. Hygiene. Preventive Medicine
spellingShingle QA75 Electronic computers. Computer science
RA0421 Public health. Hygiene. Preventive Medicine
Jimoh, Rasheed Gbenga
Abisoye, Opeyemi Aderiike
Uthman, Muhammed Mubashir Babatunde
Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection
description Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases prediction, and proneness to local minimum errors in finding reliable testing output due to complexity of features in the feature space, which is a black box in nature. This study adopted a stacking method of heterogeneous ensemble learning of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict multiclass, symptomatic, and climatic malaria infection. ANN produced 48.33 percent accuracy, 60.61 percent sensitivity, and 45.58 percent specificity. SVM with Gaussian kernel function gave better performance results of 85.60 percent accuracy, 84.06 percent sensitivity, and 86.09 percent specificity. Consequently, to improve prediction performance, a stacking method was introduced to ensemble SVM with ANN. The proposed ensemble malaria model was tuned on different thresholds at a threshold value of 0.60, the ensemble model gave an optimum accuracy of 99.86 percent, sensitivity 100 percent, specificity 98.68 percent, and mean square error 0.14. The ensemble model experimental results indicated that stacked multiple classifiers produced better results than a single model. This research demonstrated the efficiency of heterogeneous stacking ensemble model on effects of climatic variations on multiclass malaria infection classification. Furthermore, the model reduced complexity, overfitting, low rate of convergence, and proneness to local minimum error problems of multiclass malaria infection in comparison to previous related models.
format Article
author Jimoh, Rasheed Gbenga
Abisoye, Opeyemi Aderiike
Uthman, Muhammed Mubashir Babatunde
author_facet Jimoh, Rasheed Gbenga
Abisoye, Opeyemi Aderiike
Uthman, Muhammed Mubashir Babatunde
author_sort Jimoh, Rasheed Gbenga
title Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection
title_short Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection
title_full Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection
title_fullStr Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection
title_full_unstemmed Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection
title_sort ensemble feed-forward neural network and support vector machine for prediction of multiclass malaria infection
publisher Universiti Utara Malaysia Press
publishDate 2022
url https://repo.uum.edu.my/id/eprint/28755/1/JICT%2021%2001%202022%20117-148.pdf
https://doi.org/10.32890/jict2022.21.1.6
https://repo.uum.edu.my/id/eprint/28755/
https://e-journal.uum.edu.my/index.php/jict/article/view/10958
https://doi.org/10.32890/jict2022.21.1.6
_version_ 1758580949217443840
score 13.211869

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