A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components
DOI:
https://doi.org/10.26438/ijcse/v9i9.6669Keywords:
Classification, chronic kidneys disease,, dimension reduction technique, ensemble mode, feature selection techniques,, genetic algorithm, principal component analysisAbstract
Diagnosis of health conditions is an incredibly difficult and significant issue in the field of medical science. Classification, dimension reduction technique (DRT), feature selection techniques (FST) play a very important role in the quick and accurate identification of disease. The chronic kidneys disease (CKD) dataset is connected into three classification methods like RF, J48 and C5.0. The proposed ensemble model (RF, J48 and C5.0) gives better accuracy i.e. 99.75% contrast with all classifiers with selected feature subset. All classification models give a better outcome with proposed PC-DRT and GA-FST when contrasted with without FST. The outcomes showed that utilizing GA-FST has computationally enhanced the classification accuracy.
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