Title: Medical data clustering based on particle swarm optimisation and genetic algorithm
Authors: Indresh Kumar Gupta; Vikash Yadav; Sushil Kumar
Addresses: Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, 208002, U.P., India ' Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, 208002, U.P., India ' Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, 208002, U.P., India
Abstract: Medical data clustering is popular scientific approach for finding hidden patterns from large medical dataset. Medical experts utilised these patterns to make clinical diagnosis for likelihood of a disease. Clustering groups the data objects of dataset into different groups based on data similarity within group is higher than other groups. In this work, a hybrid PSO-GA algorithm is developed for medical data clustering based on 'particle swarm optimisation (PSO) and genetic algorithm (GA)'. Hybrid PSO-GA performance has examined against K-means, PSO and GA with respect to six popular medical datasets namely iris, thyroid, breast cancer, heart, diabetes and pima adopted from UCI machine learning repository over three criterion namely, i.e., sum of intra cluster distance, error rate and CPU running time. Tabular and graphical results of simulation confirm hybrid PSO-GA technique for medical data clustering is superior against K-means, PSO and GA.
Keywords: medical data clustering; particle swarm optimisation; PSO; genetic algorithm; influence factor; clustering metric.
International Journal of Advanced Intelligence Paradigms, 2019 Vol.14 No.3/4, pp.345 - 358
Received: 17 Apr 2018
Accepted: 10 May 2018
Published online: 06 Nov 2019 *