Abstract

The ensemble technology in machine learning can be used for classification purposes and it is one of the most challenging tasks for the researchers who work for improving accuracy in disease predictions. This paper propose the hybrid model that is combination of Particle Swarm Optimization (PSO), K-means clustering and ensemble classifier for classification of five different types of medical diseases. The proposed hybrid model uses PSO technique for reducing the number of features from datasets, a k-means clustering approach for reducing the instances from dataset and ensemble classifiers is used to classify the different types of diseases from five different types of datasets. The ensemble classifier uses voting scheme to ensemble the individuals base classifiers like Multilayer Perceptron (MLP), Adaboost, Bagging, Fuzzy Unordered Rule of Induction Algorithm (FURIA), and Random Forest(RF) with Naive Bayes classified where Naive Bayes is considered as a meta learner. The experiment results reveal that the combination of the ensemble classifier with PSO and k-means algorithm is an efficient novel method for disease predictions. The improved accuracy was found in between 95% to 100%. The results are evaluated and also compared with different existing models which showed better performance than others.

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