Particle Swarm Optimization based Support Vector Machine for Diabetes Mining
DOI:
https://doi.org/10.26438/ijcse/v6i8.434439Keywords:
Data Mining, Particle Swarm Optimization, Suppport Vector Machine, Diabetes MiningAbstract
Data mining is the computational procedure for discovering routines within big files portions ("big files") pertaining to techniques in the intersection involving synthetic contemplating capability, unit learning, data, as well as collection programs. In this paper, we have proposed a new method in order to improve the accuracy of diabetes classification rate. The proposed technique have integrated Particle swarm optimization (PSO) with support vector machine (SVM) based machine learning technique. The proposed technique also verified by using the various standard diabetes classification data sets. The comparison drawn among the proposed and the existing technique based upon the various standard quality metrics of the data mining. Experimental results indicate that the proposed algorithm is more efficient than existing techniques
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