Density based fuzzy support vector machine: application to diabetes dataset
In this work, we propose a deep prediction diabetes system based on a new version of the support vector machine optimization model. First, we determine three types of patients (noisy, cord, and interior) basing on specific parameters. Second, we equilibrate the clinical data sets by suppressing noisy and cord patients. Third, we determine the support vectors by solving an optimization program with a reasonable size.