An efficient ALO-based ensemble classification algorithm for medical big data processing
by Saravana Kumar Ramachandran; Parasuraman Manikandan
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 13, No. 1, 2021

Abstract: In this paper, we indented to propose a consolidated feature selection and ensemble-based classification strategy to diminish the medical big data. Here, the proposed system will be the joint execution of both the ant lion optimiser (ALO) and ensemble classifier. So as to limit the impact of an imbalanced healthcare dataset, ALO is used for the optimal feature selection process. The optimised feature sets are classified by utilising the ensemble classification technique. The ensemble classification method uses the diversity of individual classification models to create better classification results. In this paper, the proposed ensemble classification algorithm used the support vector machine (SVM), and recurrent neural network (RNN) classifier and the results of every classifier were consolidated by the majority voting technique. It was watched that the proposed ensemble technique got promising classification accuracy contrasted and other ensemble strategies. This ensemble system can administer datasets, as quick as required giving the imperative help to viably perceive the underrepresented class. The proposed approach will diminish the big medical data precisely and productively. The simulation result shows that the proposed method has better classification when compared with the single classifiers such as random forest (RF), SVM and naïve Bayes classifier.

Online publication date: Fri, 18-Dec-2020

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