mailto:uumlib@uum.edu.my 24x7 Service; AnyTime; AnyWhere

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

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

[thumbnail of JICT 21 01 2022 117-148.pdf]
Preview
PDF - Published Version
Available under License Attribution 4.0 International (CC BY 4.0).

Download (1MB) | Preview

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Artificial neural network, data mining, ensemble, malaria infection, support vector machine.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: College of Arts and Sciences
Depositing User: Mrs Nurin Jazlina Hamid
Date Deposited: 27 Jul 2022 01:56
Last Modified: 08 Feb 2023 01:28
URI: https://repo.uum.edu.my/id/eprint/28755

Actions (login required)

View Item View Item