Ibrahim Shehi Shehu

Work place: Federal University of Technology, Department of Computer Science, Minna, Nigeria



Research Interests: Mathematics of Computing, Computing Platform


Ibrahim Shehi Shehu currently lectures in the Department of Computer Science, Federal University of Technology, Minna, Niger State, Nigeria. He obtained his M.Sc. degree in Computer Science and Entrepreneurship from the University of Nottingham in 2012. His current research motive is on the application of Decision Support Systems (DSS), Expert systems (such as Adaptive Hypermedia Systems) in education and Multi agent systems. Other research interest include; soft computing, and usability studies.

Author Articles
A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

By Enesi Femi Aminu Emmanuel Onyebuchi Ogbonnia Ibrahim Shehi Shehu

DOI: https://doi.org/10.5815/ijmsc.2016.04.06, Pub. Date: 8 Nov. 2016

High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.

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