An Effective Chronic Disease Prediction using Multi-Objective Firefly Optimisation Random Forest Algorithm

In recent years, the solitary reasons for mortality in the world are chronic diseases such as heart disease, diabetes, and chronic kidney disease. These diseases should be diagnosed earlier; however, the technique is costly as well as it leads to many complications. Considering the complexity, datamining performs a major part in accurately classifying chronic disease. A new approach to classify chronic disease is by merging the multi-objective firefly optimisation algorithm (MOFFA) and random forest (RF). The main goal is generating an efficient and heterogeneous decision trees, while determining the optimum training sets to run at the same time. Rather utilising traditional approach like bootstrap, multi-objective firefly optimisation algorithm and random forest algorithm are proposed in this method. As a result, to train random forest, various training sets are generated with alternative instances and attributes. As a result, the performance of random forests can be improved and thus the prediction accuracy. The effectiveness of the proposed method is explored by juxtaposing the effectiveness of the proposed method with other classifiers for different datasets. The proposed work is tested on six UCI datasets. According to the findings, the proposed MOFFA-RF algorithm surpass other classifiers by the accuracy of 88% on CKD, 87% on CVD, 82% on diabetes, 88% on hepatitis, 88% on WBC, and 76% on ILPD.