Innovative ensemble machine learning model for enhanced prediction of water-related diseases: A comparative analysis with Naive Bayes to enhance accuracy.

This work aims to improve the accuracy of cholera disease prediction using water characteristics by utilizing a new technique called Ensemble Learning (EL) instead of Naive Bayes (NB). The Kaggle database management system provided the dataset for this study. A sample size of 3964 individuals (equally divided between Group 1 and Group 2) and a computation utilizing a G-power of 0.8, α=0.05, and β=0.2 were used to achieve improved prediction accuracy for cholera disease. The significance level of the test yielded a value of p=0.001 (p<0.05) with a 95% confidence interval using independent sample T-tests. Using the same number of data samples (N=1982), the Ensemble Learning (EL) and Naive Bayes (NB) algorithms were used to predict cholera cases; nevertheless, EL performed more accurately. The success rate of the suggested EL algorithm was 96.30%, higher than that of the NB classifier, which had an 89.50% success rate. At p=0.034, the statistical significance of this disagreement has been shown. The Ensemble Learning (EL) model performs better than the Naive Bayes model in the assessment of cholera infection performance. [ABSTRACT FROM AUTHOR]