Open AccessMalaria Prediction Model Using Machine Learning Algorithms
Author(s) -
Yusuf Aliyu Adamu
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i10.5655
Subject(s) - malaria , naive bayes classifier , random forest , machine learning , government (linguistics) , artificial intelligence , pandemic , support vector machine , logistic regression , computer science , disease , public health , case fatality rate , environmental health , medicine , covid-19 , population , linguistics , philosophy , nursing , pathology , infectious disease (medical specialty) , immunology
Measures have been taking to ensure the safety of individuals from the burden of vector-borne disease but it remains the causative agent of death than any other diseases in Africa. Many human lives are lost particularly of children below five years regardless of the efforts made. The effect of malaria is much more challenging mostly in developing countries. In 2019, 51% of malaria fatality happen in Africa which it increased by 20% in 2020 due to the covid-19 pandemic. The majority of African countries lack a proper or a sound health care system, proper environmental settlement, economic hardship, limited funding in the health sector, and absence of good policies to ensure the safety of individuals. Information has to become available to the peoples on the effect of malaria by making public awareness program to make sure people become acquainted with the disease so that certain measure can be maintained. The prediction model can help the policymakers to know more about the expected time of the malaria occurrence based on the existing features so that people will get to know the information regarding the disease on time, health equipment and medication to be made available by government through it policy. In this research weather condition, non-climatic features, and malaria cases are considered in designing the model for prediction purposes and also the performance of six different machine learning classifiers for instance Support Vector Machine, K-Nearest Neighbour, Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes is identified and found that Random Forest is the best with accuracy (97.72%), AUC (98%) AUC, and (100%) precision based on the data set used in the analysis.