Early-Stage Diabetes Risk Detection Using Data Mining Techniques With Particle Swarm Optimization
DOI:
https://doi.org/10.26438/ijcse/v9i9.6365Keywords:
Classification, C4.5, feature Selection technique, Particle Swarm OptimizationAbstract
In this study feature Selection technique (FST) namely Particle Swarm Optimization (PSO) is used to optimize the features of diabetes datasets. There are different types of classifiers that give low performances. So we need an FST to combined classifier may be required for best results. We used FST to improve the overall performance of the classification model. Classification of diabetes dataset classifier C4.5 and Support Vector Machine (SVM) is applied. The selected feature of diabetes is applied to classifiers and a comparative study was conducted. The experimental outcome reveals that the C4.5 is performed better with selected features compared to other models.
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