Research Article Open Access

A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis

Santi Wulan Purnami, Abdullah Embong, Jasni Mohd Zain and S. P. Rahayu


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.

Journal of Computer Science
Volume 5 No. 12, 2009, 1003-1008


Submitted On: 2 October 2009 Published On: 31 December 2009

How to Cite: Purnami, S. W., Embong, A., Zain, J. M. & Rahayu, S. P. (2009). A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis. Journal of Computer Science, 5(12), 1003-1008.

  • 26 Citations



  • Smooth support vector machine
  • diabetes disease diagnosis
  • classification