Predicting Malaria Using Logistic Regression Model

AMINU ALIYU; Shuaibu Yau; Farouk Musa Aliyu

AMINU ALIYU Federal University Birnin Kebbi
Shuaibu Yau Department of Computer Science, Federal University Birnin Kebbi
Farouk Musa Aliyu Department of Computer Science, Federal University Birnin Kebbi

Keywords: Confusion Matrix, Logistic Regression Model, and Malaria

Abstract

Today Malaria is considered to be one of the major causes of mortality in the world. The availability of malaria patient data in our hospital presented an opportunity of providing precautionary system for use in rural community through the use of developed model. This study developed a logistic regression model using 701 dataset obtained from Hospital in Adamawa, Nigeria in 2020.The study considered the parameters of fever, headache, Nausea, and vomiting as clinical manifestation symptom of malaria. The study finds out that all the four parameters used in the study were significant, and at any giving new instance of the parameters, the model can correctly classify the patient. The developed model was checked for accuracy using confusion matrix and the model demonstrated an accuracy of 86.43%.

Author Biographies

Shuaibu Yau, Department of Computer Science, Federal University Birnin Kebbi

Department of Computer Science, Lecturer

Farouk Musa Aliyu, Department of Computer Science, Federal University Birnin Kebbi

Lecturer II at Federal University Birnin Kebbi

References

Chotivanich, K., Silamut, K., & Day, N. P. (2007). Laboratory diagnosis of malaria infection-A short review of methods. New Zealand Journal of Medical Laboratory Science, 61(1), 4.
Dangare, C. S., & Cse, M. E. (2012). Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques. 47(10), 44–48.
Gu, X., Chen, H., & Yang, B. (2015). Heterogeneous data mining for planning active surveillance of malaria. Proceedings of the ASE BigData & SocialInformatics 2015, 34. ACM.
J.M.T, Hendriksen, G.J, G. (2013). Diagnostic and prognostic prediction models. 11, 129–141. https://doi.org/10.1111/jth.12262
Jothi, N., Rashid, N. A., & Husain, W. (2015). Data Mining in Healthcare - A Review. Procedia Computer Science, 72, 306–313. https://doi.org/10.1016/j.procs.2015.12.145
K.Srinivas, Rani, B. K., & Govrdhan, A. (2010). Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. 02(02), 250–255.
Mohapatra, B. N., Jangid, S. K., & Mohanty, R. (2014). GCRBS score: a new scoring system for predicting outcome in severe falciparum malaria. The Journal of the Association of Physicians of India, 62(1), 14–17.
Pirnstill, C. W., & Coté, G. L. (2015). Malaria Diagnosis Using a Mobile Phone Polarized Microscope. Scientific Reports, 5, 13368. https://doi.org/10.1038/srep13368
Sharma, V., Kumar, A., Panat, L., & Karajkhede, G. (2016). Malaria Outbreak Prediction Model Using Machine Learning. (January).
Shmueli, G. (2010). To Explain or to Predict ? 25(3), 289–310. https://doi.org/10.1214/10-STS330
Stauffer, W., & Fischer, P. R. (2003). Diagnosis and Treatment of Malaria in Children. 55905(May).
Vitorino, R., De, A., Mendonça, D., & Goreti, M. (2011). Severe Plasmodium falciparum malaria. 23(3), 358–369.