Factors Affecting Cholera Disease and Its Machine Learning Solution: A Review

Factors Affecting Cholera Disease and Its Machine Learning Solution: A Review
Author(s):
AHMAD HAUWA AMSHI, RAJESH PRASAD
Keywords:
Cholera, Climate change, Machine learning, Logistic regression, and Artificial Neural Network.
Abstract
The number of African countries reporting indigenous cholera cases as per the report of WHO increased from 24 in 1971 to 30 in 1998 and 36 in 2008, a new high. The latest 2018 cholera epidemic in Nigeria resulted in over 45 thousand cases and a Case Fatality Rate (CFR) of 1. In the year 2021, Nigeria reported a total of 93,362 suspected cholera cases, including 3,283 cholera-related deaths; the highest cases of cholera from the 32 states and the Federal Capital Territory. Cholera is one of the primary causes of morbidity and mortality in Nigeria, from small outbreaks to large epidemics. Because of the limited workforce in the Nigerian healthcare sector and the usage of manual methods, there is an urgent need to build a viable cholera prediction model for early warning mechanisms. This paper investigates the factors affecting the cholera disease within Nigeria and the machine learning solution to it.
Article Details
Unique Paper ID: 155370 Publication Volume & Issue: Volume 8, Issue 10 Page(s): 20 - 24
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