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Predictive Model of Pediatric HIV/AIDS Survival in Nigeria using Support Vector Machine

Olayemi Olufunke C., Olasehinde Olayemi O., Agbelusi O.. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Olayemi Olufunke C., Olasehinde Olayemi O., Agbelusi O.
10.5120/cae2016652349

Olayemi Olufunke C., Olasehinde Olayemi O. and Agbelusi O.. Predictive Model of Pediatric HIV/AIDS Survival in Nigeria using Support Vector Machine. Communications on Applied Electronics 5(8):29-36, August 2016. BibTeX

@article{10.5120/cae2016652349,
	author = {Olayemi Olufunke C. and Olasehinde Olayemi O. and Agbelusi O.},
	title = {Predictive Model of Pediatric HIV/AIDS Survival in Nigeria using Support Vector Machine},
	journal = {Communications on Applied Electronics},
	issue_date = {August 2016},
	volume = {5},
	number = {8},
	month = {Aug},
	year = {2016},
	issn = {2394-4714},
	pages = {29-36},
	numpages = {8},
	url = {http://www.caeaccess.org/archives/volume5/number8/641-2016652349},
	doi = {10.5120/cae2016652349},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper is focused on the development of a predictive model for the classification of HIV/AIDS survival among Nigerian pediatric patients located in south-western Nigeria using supervised machine learning. Following the identification of the risk factors of HIV/AIDS survival from the review of literature and expert medical physicians, the case files of patients were used to collect information about the distribution of the risk factors and the HIV/AIDS survival status of pediatric patients selected at two hospitals in south-western Nigeria. The predictive model was formulated using the sequential minimal optimization (SMO) algorithm implemented by the support vector machine (SVM) – a binary classification algorithm based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The SVM classifier performed well in the classification of the survival of pediatric HIV/AIDS patients with an accuracy of 97.7%. The predictive model developed can be useful to medical practitioners especially in the area of decision support regarding the survival of HIV/AIDS pediatric patients in Nigeria.

References

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Keywords

HIV/AIDS survival, pediatric patients, support vector machines (SVM), sequential minimal optimization (SMO), predictive modeling.