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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Autores principales:	Opeyemi Aderiike Abisoye, Rasheed Gbenga Jimoh, Muhammed Uthman Mubashir Babatunde Uthman
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2021
Materias:	artificial neural network data mining ensemble malaria infection support vector machine Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/da48e5e01e164becb0f9809e258ca85f
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spelling	oai:doaj.org-article:da48e5e01e164becb0f9809e258ca85f2021-11-14T08:57:20ZEnsemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection10.32890/jict2022.21.1.61675-414X2180-3862https://doaj.org/article/da48e5e01e164becb0f9809e258ca85f2021-11-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2022.21.1.6https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Globally, 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. Opeyemi Aderiike AbisoyeRasheed Gbenga JimohMuhammed Uthman Mubashir Babatunde UthmanUUM Pressarticleartificial neural networkdata miningensemblemalaria infectionsupport vector machineInformation technologyT58.5-58.64ENJournal of ICT, Vol 21, Iss 1, Pp 117-148 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network data mining ensemble malaria infection support vector machine Information technology T58.5-58.64
spellingShingle	artificial neural network data mining ensemble malaria infection support vector machine Information technology T58.5-58.64 Opeyemi Aderiike Abisoye Rasheed Gbenga Jimoh Muhammed Uthman Mubashir Babatunde Uthman 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	Opeyemi Aderiike Abisoye Rasheed Gbenga Jimoh Muhammed Uthman Mubashir Babatunde Uthman
author_facet	Opeyemi Aderiike Abisoye Rasheed Gbenga Jimoh Muhammed Uthman Mubashir Babatunde Uthman
author_sort	Opeyemi Aderiike Abisoye
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	UUM Press
publishDate	2021
url	https://doaj.org/article/da48e5e01e164becb0f9809e258ca85f
work_keys_str_mv	AT opeyemiaderiikeabisoye ensemblefeedforwardneuralnetworkandsupportvectormachineforpredictionofmulticlassmalariainfection AT rasheedgbengajimoh ensemblefeedforwardneuralnetworkandsupportvectormachineforpredictionofmulticlassmalariainfection AT muhammeduthmanmubashirbabatundeuthman ensemblefeedforwardneuralnetworkandsupportvectormachineforpredictionofmulticlassmalariainfection
_version_	1718429806773665792

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

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