Hybrid ANFIS-genetic algorithm based forecasting model for predicting Cholera-waterborne disease. (16th December 2020) - Kaur, Sandeep; Chahal, Kuljit Kaur
Cholera is one of the rapidly spreading waterborne diseases which are caused by bacteria named Vibrio Cholerae. As population grows, so as the data related to patients, doctors and health staff. So far, different types of machine learning models have been proposed for classification of Cholera infection. However, the majority of these suffers from pre-mature convergence and stuck in local optimal issues. In this paper, ANFIS-GA based forecasting model for the prediction of Cholera virus has been proposed. In the proposed model, non-dominated sorting genetic algorithm (NSGA) is used to tune hyper-parameters of ANFIS. The comparisons are done among the designed NSGA-ANFIS and the existing models on the benchmark Cholera dataset. Performance analysis illustrates that the designed NSGA-ANFIS model performs significantly better than the existing models such as ANFIS, PSO-ANFIS and GA-ANFIS in terms of accuracy, sensitivity, kappa statistics, specificity and F-measure, as 99.2%, 99.04%, 99.11%, 99.49% and 98.85%, respectively.