Classification Of Diabetes Patients Using Kernel Based Support Vector Machines
| Autor: | G. A. Pethunachiyar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science information science 02 engineering and technology medicine.disease Machine learning computer.software_genre Support vector machine 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Diabetes mellitus Kernel (statistics) 0202 electrical engineering electronic engineering information engineering medicine Human pressure 030211 gastroenterology & hepatology 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | 2020 International Conference on Computer Communication and Informatics (ICCCI). |
| DOI: | 10.1109/iccci48352.2020.9104185 |
| Popis: | Diabetes mellitus (DM) is a collection of metabolic diseases that influence the human pressure significantly worldwide. Detection of patient with diabetes at early stage is the most crucial task and helps to avoid the risk of the people from the diseases that lead to cause death. In diabetes research, the machine learning plays the important role in detecting the diseases at an early stage. There are more machine learning algorithms used for the research. Support Vector Machines (SVM) is the most successful and widely used algorithm. In this paper, SVM with different kernel functions are applied. SVM with linear kernel showed the highest accuracy value for the classification of diabetes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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