Article: Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques Journal: International Journal of Computing Science and Mathematics (IJCSM) 2023 Vol.17 No.3 pp.295 - 307 Abstract: This paper presents a hybrid model based on particle swarm optimisation (PSO), K-means, and self organising map (SOM), combined with different data mining-based classifiers for the identification and classification of various health-related diseases. The main contribution of this research work is to develop a robust and computationally efficient predictive hybrid model using K-means and SOM unsupervised clustering techniques to facilitate the classification of data. The clustering algorithms help to reduce the wrong instances from the database, while the PSO is used to optimise the features of datasets. Both of these methods help to improve classification accuracy and reduce uncertainty. The proposed hybrid-based model diagnosed different diseases, namely chronic kidney disease (CKD), breast cancer, and Hepatitis disease in a better way. The results confirmed that the proposed hybrid system achieved better performance in terms of measures: accuracy, sensitivity, and specificity. Inderscience Publishers - linking academia, business and industry through research

Title: Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques

Authors: Rashmi Gupta; Akhilesh Kumar Shrivas; Ragini Shukla

Addresses: Department of Information Technology and Computer Application, Dr. C.V. Raman University, Kota, Bilaspur, C.G., 495113, India ' Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, C.G., 495009, India ' Department of Information Technology and Computer Application, Dr. C.V. Raman University, Kota, Bilaspur, C.G., 495113, India

Abstract: This paper presents a hybrid model based on particle swarm optimisation (PSO), K-means, and self organising map (SOM), combined with different data mining-based classifiers for the identification and classification of various health-related diseases. The main contribution of this research work is to develop a robust and computationally efficient predictive hybrid model using K-means and SOM unsupervised clustering techniques to facilitate the classification of data. The clustering algorithms help to reduce the wrong instances from the database, while the PSO is used to optimise the features of datasets. Both of these methods help to improve classification accuracy and reduce uncertainty. The proposed hybrid-based model diagnosed different diseases, namely chronic kidney disease (CKD), breast cancer, and Hepatitis disease in a better way. The results confirmed that the proposed hybrid system achieved better performance in terms of measures: accuracy, sensitivity, and specificity.

Keywords: hybrid model; data mining; SOM; self-organising map; PSO; particle swarm optimisation; classification; feature selection; clustering; chronic kidney disease; decision tree; machine learning.

DOI: 10.1504/IJCSM.2023.131438

International Journal of Computing Science and Mathematics, 2023 Vol.17 No.3, pp.295 - 307

Received: 04 Apr 2021
Accepted: 10 Jan 2022
Published online: 13 Jun 2023 *

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Title: Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques

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