Comparative Study on the Prediction of Symptomatic and Climatic based Malaria Parasite Counts Using Machine Learning Models

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Opeyemi A. Abisoye 1,* Rasheed G. Jimoh 2

1. Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, P.M.B. 65, Minna, Niger State, Nigeria

2. Department of Computer Science, Faculty of Communication and Information Science, University of Ilorin, P.M.B.1515, Nigeria

* Corresponding author.


Received: 1 Dec. 2017 / Revised: 13 Jan. 2018 / Accepted: 30 Jan. 2018 / Published: 8 Apr. 2018

Index Terms

Malaria, Prediction, Artificial Neural Network (ANN), Support Vector Machine (SVM), Symptomatic, Climatic


Dynamics of Malaria parasite diagnosis is complex and been widely studied. Research is on-going on the effects of climatic variations on symptomatic malaria infection. Malaria diagnosis can be asymptomatically or symptomatically low, mild and high. An analytical program is needed to detect individual malaria parasite counts from complex network of several infection counts. This study adopted the experimental malaria parasite counts collected from selected hospitals in Minna Metropolis, Niger State, Nigeria and Climatic data collected at the time the experiment was conducted from NECOP, Bosso, FUT Minna, Niger State, Nigeria. One thousand and two hundred (1,200) experimental data were collected and two classifiers Support Vector Machine (SVM), Artificial Neural Network (ANN) do the prediction. Experimental results indicated that SVM produced Accuracy 85.60%, Sensitivity 84.06%, Specificity 86.49%, False Positive Rate(FPr) 0.1351% and False Negative Rate(FNr) 0.1594% than Neural Network model of Accuracy 48.33%, Sensitivity 60.61%, Specificity 45.48%, low False Positive Rate (FPr) 0.5442% and False Negative Rate(FNr) 0.3939% as depicted in their respective confusion matrix.

Cite This Paper

Opeyemi A. Abisoye, Rasheed G. Jimoh, " Comparative Study on the Prediction of Symptomatic and Climatic based Malaria Parasite Counts Using Machine Learning Models", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.4, pp. 18-25, 2018. DOI:10.5815/ijmecs.2018.04.03


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