TY - JOUR AU - Purnami, Santi Wulan AU - Embong, Abdullah AU - Zain, Jasni Mohd AU - Rahayu, S. P. PY - 2009 TI - A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis JF - Journal of Computer Science VL - 5 IS - 12 DO - 10.3844/jcssp.2009.1003.1008 UR - https://thescipub.com/abstract/jcssp.2009.1003.1008 AB - 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.