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Hybridized Model using Clustering with Ensemble Classifier for Classification of Diseases

by Rashmi Gupta, Akhilesh Kumar Shrivas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 72
Year of Publication: 2025
Authors: Rashmi Gupta, Akhilesh Kumar Shrivas
10.5120/ijca2025924578

Rashmi Gupta, Akhilesh Kumar Shrivas . Hybridized Model using Clustering with Ensemble Classifier for Classification of Diseases. International Journal of Computer Applications. 186, 72 ( Mar 2025), 42-51. DOI=10.5120/ijca2025924578

@article{ 10.5120/ijca2025924578,
author = { Rashmi Gupta, Akhilesh Kumar Shrivas },
title = { Hybridized Model using Clustering with Ensemble Classifier for Classification of Diseases },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 72 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 42-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number72/hybridized-model-using-clustering-with-ensemble-classifier-for-classification-of-diseases/ },
doi = { 10.5120/ijca2025924578 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-25T22:41:27.368704+05:30
%A Rashmi Gupta
%A Akhilesh Kumar Shrivas
%T Hybridized Model using Clustering with Ensemble Classifier for Classification of Diseases
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 72
%P 42-51
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Particle swarm optimization (PSO) Machine learning Ensemble classifier Classification Clustering Multilayer Perceptron (MLP) Bagging Adaboost Random forest (RF) Voting FURIA

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