Opeyemi A. Abisoye; Rasheed G. Jimoh

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

Full Text (PDF, 960KB), PP.18-25

Views: 0 Downloads: 0

Author(s)

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.

DOI: https://doi.org/10.5815/ijmecs.2018.04.03

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

Abstract

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

Reference

[1]Mueller, I., Galinski, M. R., Baird, J.K., Carlton, J. M., Kochar, D. K., Alonso, P. L., & del Portillo, H. A. (2009). Key gaps in the knowledge of Plasmodium vivax, a neglected human malaria parasite. The Lancet infectious diseases, 9(9), 555-566.
[2]Bannister, L., & Mitchell, G. (2003). The ins, outs and roundabouts of malaria. Trends in parasitology, 19(5), 209-213.
[3]Singh, S. (2006). New developments in diagnosis of leishmaniasis. Indian Journal of Medical Research, 123(3), 311.
[4]Olaronke, I., & Oluwaseun, O. (2016). An Ontology Based Remote Patient Monitoring Framework for Nigerian Healthcare System. International Journal of Modern Education and Computer Science, (IJMECS), 8(10), 17.
[5]White, N. J. (2004). Antimalarial drug resistance. Journal of clinical investigation, 113(8), 1084.
[6]Cherif, A. H., Movahedzadeh, F., Michel, L., Hill, A., & Jedlicka, D. M.(2011) Environmental Release of Genetically Engineered Mosquitoes
[7]Bottius, E., Guanzirolli, A., Trape, J. F., Rogier, C., Konate, L., & Druilhe, P. (1996). Malaria: even more chronic in nature than previously thought; evidence for subpatent parasitaemia detectable by the polymerase chain reaction. Transactions of the Royal Society of Tropical Medicine and Hygiene, 90(1), 15-19.
[8]Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press
[9]Ahmad, Munir, and Shabib Aftab. "International Journal of Modern Education and Computer Science (IJMECS)." (2017).
[10]Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
[11]K., Kigozi, R., Charland, K., Dorsey, G., Kamya, M., & Buckeridge, D. (2013). Predicting Malaria in a Highly Endemic Country using Environmental and Clinical Data Sources. Online journal of public health informatics, 6(1).
[12]Paokanta, P., Ceccarelli, M., & Srichairatanakoo, S. (2010, November). The efficiency of data types for classification performance of Machine Learning Techniques for screening β-Thalassemia. In Applied Sciences in Biomedical and Communication Technologies (ISABEL), 2010 3rd International Symposium of Applied Sciences in Biomedical and Communication Techn. (pp. 1-4)
[13]Martínez-Martínez, J. M., Escandell-Montero, P., Barbieri, C., Soria-Olivas, E., Mari, F., Martínez-Sober, M., ...& Gatti, E. (2014). Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques.Computer methods and programs in biomedicine, 117(2), 208-217.
[14]GÜL, S., UÇAR, M. K., ÇETİNEL, G., BERGİL, E., & BOZKURT, M. R. (2017). Automated Pre-Seizure Detection for Epileptic Patients Using Machine Learning Methods. International Journal of Image, Graphics & Signal Processing, 9(7).
[15]Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015).Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
[16]Asadi, H., Dowling, R., Yan, B., & Mitchell, P. (2014).Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PloS one, 9(2), e88225.
[17]Danger, R., Segura-Bedmar, I., Martínez, P., & Rosso, P. (2010).A comparison of machine learning techniques for detection of drug target articles. Journal of biomedical informatics, 43(6), 902-913.
[18]Urquiza, J. M., Rojas, I., Pomares, H., Herrera, J., Florido, J. P., Valenzuela, O., & Cepero, M. (2012).Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification. Computers in biology and medicine, 42(6), 639-650.
[19]Caravaca Moreno, J., Soria Olivas, E., Bataller Mompeán, M., Serrano López, A. J., Such Miquel, L., Vila Francés, J., & Guerrero Martínez, J. F. (2014). Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation. Computers in Biology and Medicine, 2014, vol. 45, num. 1, p. 1-7.
[20]Worner, S. P., & Gevrey, M. (2006). Modeling global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology, 43(5), 858-867.
[21]Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
[22]Zinszer, K., Kigozi, R., Charland, K., Dorsey, G., Kamya, M., & Buckeridge, D. (2013). Predicting Malaria in a Highly Endemic Country using Environmental and Clinical Data Sources. Online journal of public health informatics, 6(1).
[23]Shruti A. & Shirgan S.S (2015). Automatic Diagnosis of Malaria Parasites Using Neural Network and Support Vector Machine., International Journal of Advanced Foundation in Computer(IJAFRC), 2, 62 -65.
[24]Chaudhari, t., & Agrawal, (2015). The Automatic Detection of Malaria Parasites for Estimating Parasitemia.
[25]Sharma, V., Kumar, A., Lakshmi Panat, D., & Karajkhede, G. (2015). Malaria Outbreak Prediction Model Using Machine Learning. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 4(12),
[26]Abisoye, Opeyemi A & Jimoh Gbenga R.(2017). Symptomatic and Climatic Based Malaria Threat Detection Using Multilevel Thresholding FeedForward Neural Network. I.J. Information Technology and Computer Science, 8, 40-46

International Journal of Modern Education and Computer Science (IJMECS)