@article {10.3844/jcssp.2009.1003.1008,
article_type = {journal},
title = {A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis},
author = {Purnami, Santi Wulan and Embong, Abdullah and Zain, Jasni Mohd and Rahayu, S. P.},
volume = {5},
number = {12},
year = {2009},
month = {Dec},
pages = {1003-1008},
doi = {10.3844/jcssp.2009.1003.1008},
url = {https://thescipub.com/abstract/jcssp.2009.1003.1008},
abstract = {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 = {Journal of Computer Science},
publisher = {Science Publications}
}