ABSTRACT
Malaria is a deadly disease killing millions of people every year. Different countries of the
world, governmental and non-governmental organizations including World Health
Organization have taken it as a challenge to address the issue of deaths associated with
malaria. Prompt and accurate diagnosis is a major key in medical field. This prompts for the
need to develop a Bayesian base approach to malaria fever diagnosis. A machine learning
technique Bayesian was used on labelled sets of malaria fever symptoms collected in malaria
dataset. The labelled database was divided into five cases of malaria and the classification
model for malaria fever diagnosis was developed using WEKA software.
The developed model has been tested and gives a classification accuracy of 66% on training
dataset while that of testing data set gives classification accuracy of 84%.
The result shows that the Bayesian is a promising approach and the system hereby recommended for use in areas where cases of malaria fever are prevalence.
ADEKUNLE, Y (2021). Development Of A Bayesian Based Approach To Malaria Fever Diagnosis. Afribary. Retrieved from https://afribary.com/works/development-of-a-bayesian-based-approach-to-malaria-fever-diagnosis
ADEKUNLE, YAYA "Development Of A Bayesian Based Approach To Malaria Fever Diagnosis" Afribary. Afribary, 20 May. 2021, https://afribary.com/works/development-of-a-bayesian-based-approach-to-malaria-fever-diagnosis. Accessed 29 Nov. 2024.
ADEKUNLE, YAYA . "Development Of A Bayesian Based Approach To Malaria Fever Diagnosis". Afribary, Afribary, 20 May. 2021. Web. 29 Nov. 2024. < https://afribary.com/works/development-of-a-bayesian-based-approach-to-malaria-fever-diagnosis >.
ADEKUNLE, YAYA . "Development Of A Bayesian Based Approach To Malaria Fever Diagnosis" Afribary (2021). Accessed November 29, 2024. https://afribary.com/works/development-of-a-bayesian-based-approach-to-malaria-fever-diagnosis