Unveiling Cholera Patterns through Machine Learning Regression for Precise Forecasting

The purpose of this research study is to conduct a explorative analysis on the feasibility of employing diverse machine learning regression algorithms such as Linear Regression (LR), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Polynomial Regression for predicting the cholera outbreaks. This research study additionally considers the environmental parameters and socioeconomic factors along with the existing cholera outbreak data to effectively comprehend the underlying cause of the cholera outbreak. This study compares the performance of various regression methods in analyzing and predicting the cholera patterns. In order to analyze the complex non-linear models, a hybrid approach of Random Forest and Polynomial Regression is used. The obtained results reveal that, the proposed model has the ability to enhance the clinical decisionmaking process and reduce the impact of cholera outbreak among people.