CHOLERA PREDICTION MODEL USING FEATURE CLUSTERING BAYESIAN TECHNIQUE

Abstract

Cholera is one the most deadly disease that is mostly caused due by poor sanitation or and drinking contaminated water or food with a bacterium called Vibrio Cholera. Many researchers have provided a solution to prevent the outbreak of cholera using various methods, the recent ones are using machine learning techniques and some mathematical methods such as mathematical epidemiological, spatial statistics, and based on association rule mining using the nonstandard distribution dataset to mention a few. These few methods are mostly used in predicting cholera outbreaks but have some limitations, such as using fewer features for prediction, waiting until certain cases are reported before getting data, based on Rainfall, based on the flowing speed of rivers, wind direction, and flood, etc. in this research a more comprehensive cholera features would be used in predicting an outbreak before it occurs based on the existing secondary dataset using The Naïve Bayesian Classification technique. The proposed model has more features and is not dependent on certain events to occur before predicting any outbreak. Python programming was used in implementing the proposed model. An accuracy of 99% was achieved and it shows it is better than the previous models used in predicting cholera outbreaks.