A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis
- 1 , Afganistan
- 2 ,
Copyright: © 2020 Santi Wulan Purnami, Abdullah Embong, Jasni Mohd Zain and S. P. Rahayu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Research on Smooth Support Vector Machine (SSVM) is an active field in data mining. Many researchers developed the method to improve accuracy of the result. This study proposed a new SSVM for classification problems. It is called Multiple Knot Spline SSVM (MKS-SSVM). To evaluate the effectiveness of our method, we carried out an experiment on Pima Indian diabetes dataset. The accuracy of previous results of this data still under 80% so far. Approach: First, theoretical of MKS-SSVM was presented. Then, application of MKS-SSVM and comparison with SSVM in diabetes disease diagnosis were given. Results: Compared to the SSVM, the proposed MKS-SSVM showed better performance in classifying diabetes disease diagnosis with accuracy 93.2%. Conclusion: The results of this study showed that the MKS-SSVM was effective to detect diabetes disease diagnosis and this is very promising compared to the previously reported results.
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- Smooth support vector machine
- diabetes disease diagnosis