Hybrid of Support Vector Machine Algorithm and K-Nearest Neighbor Algorithm to Optimize the Diagnosis of Eye Disease
| Autor: | Sumita Wardani, Sawaluddin, Poltak Sihombing |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
genetic structures
Computer science Eye disease ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION information science medicine.disease Hybrid algorithm eye diseases k-nearest neighbors algorithm Support vector machine ComputingMethodologies_PATTERNRECOGNITION Support vector machine algorithm medicine Neighbor algorithm sense organs Combination method Algorithm |
| Zdroj: | 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT). |
| DOI: | 10.1109/mecnit48290.2020.9166599 |
| Popis: | In order to optimize the diagnosis of eye disease, the authors want to propose the combination of Support Vector Machine and K-Nearest Neighbor Algorithm to Optimize Diagnosis of Eye Disease. This is a combination method of SVM to get a better classification to diagnose the eye disease. The problem is how to get a better classification to diagnose eye disease. In this research, the hybrid of SVM-KNN is more accurate to classify the eye disease than SVM itself. The accuracy using the combination SVM-KNN algorithm is 94.67%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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