Model Development for Predicting the Occurrence of Benign Laryngeal Lesions using Support Vector Machine: Focusing on South Korean Adults Living in Local Communities

The disease is a consequence of interactions between many complex risk factors, rather than a single cause. Therefore, it is necessary to develop a disease prediction model by using multiple risk factors instead of using a single risk factor. The objective of this study was to develop a model for predicting the occurrence of benign laryngeal lesions based on support vector machine (SVM) using ear, nose and throat (ENT) data from a national-level survey and to provide a basis for selecting high-risk groups and preventing a voice disorder. This study targeted 16,938 adults (≥19years) who participated in the ENT examination among the people who completed the Korea National Health and Nutrition Examination Survey from 2010 to 2012. This study compared the prediction power of the Gauss function, which was used for this study, with that of a linear algorithm, that of a polynomial algorithm, and that of a sigmoid algorithm. Moreover, four kernels were divided into C-SVM and Nu-SVM to compare the prediction accuracy of C-SVM with that of Nu-SVM. The ‘benign laryngeal lesion prediction model’ based on SVM could derive preventive factors and risk factors. The final prediction rate of this SVM using 479 support vectors was 97.306. The fitness results indicated that the difference between C-SVM and Nu-SVM was not large in the benign laryngeal lesion prediction model. In terms of kernel type, the prediction accuracy of Gauss kernel was the highest and the prediction accuracy of the sigmoid kernel was the lowest. The results of this study will provide an important basis for preventing and managing benign laryngeal lesions.

@article{Byeon2018,
  title     = {Model Development for Predicting the Occurrence of Benign Laryngeal Lesions using Support Vector Machine: Focusing on South Korean Adults Living in Local Communities},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {10},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Haewon Byeon},
  doi       = {10.14569/IJACSA.2018.091027},
  url       = {https://doi.org/10.14569/IJACSA.2018.091027}
}