Density based fuzzy support vector machine: application to diabetes dataset

: pp. 747–760
Received: May 23, 2021
Accepted: June 07, 2021
Engineering Science Laboratory (LSI), Faculty Polydisciplinary of Taza, USMBA, Morocco
Engineering Science Laboratory (LSI), Faculty Polydisciplinary of Taza, USMBA, Morocco

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. Our system is performed on the well-known diabetes dataset PIMA.  The experimental results show that the proposed method improves the prediction accuracy and the proposed system significantly outperforms all other versions of SVM as well as literature methods of classification.

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Mathematical Modeling and Computing, Vol. 8, No. 4, pp. 747–760 (2021)