Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels
Diabetes is one of the most serious health challenges in both developed and developing countries. Early detection and accurate diagnosis of diabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease has gradually increased. A promising classific...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2016-07-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | http://ijtech.eng.ui.ac.id/article/view/1893 |
| _version_ | 1797966810411171840 |
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| author | Abdul Azis Abdillah Suwarno Suwarno |
| author_facet | Abdul Azis Abdillah Suwarno Suwarno |
| author_sort | Abdul Azis Abdillah |
| collection | DOAJ |
| description | Diabetes is one of the most serious health challenges in both developed and developing countries. Early detection and accurate diagnosis of diabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease has gradually increased. A promising classification technique in machine learning is the use of support vector machines in combination with radial basis function kernels (SVM-RBF). In this study, we used SVM-RBF to predict diabetes. The study used a Pima Indian diabetes dataset from the University of California, Irvine (UCI) Machine Learning Repository. The subjects were female and ? 21 years of age at the time of the index examination. Our experiment design used 10-fold cross-validation. Confusion matrix and ROC were used to calculate performance evaluation. Based on the experimental results, the study demonstrated that SVM-RBF shows promise in aiding diagnosis of Pima Indian diabetes disease in the early stage. |
| first_indexed | 2024-04-11T02:21:14Z |
| format | Article |
| id | doaj.art-1eb2e29f5bff46c3a5293e2685b9a282 |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-11T02:21:14Z |
| publishDate | 2016-07-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-1eb2e29f5bff46c3a5293e2685b9a2822023-01-02T23:35:11ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002016-07-017584985810.14716/ijtech.v7i5.18931893Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function KernelsAbdul Azis Abdillah0Suwarno Suwarno1Department of Mechanical Engineering, Jakarta State Polytechnic, Kampus Baru UI, Depok, 16424, Indonesia Department of Mathematics Education, STKIP Surya, Tangerang, 15810, IndonesiaDepartment of Mathematics Education, STKIP Surya, Tangerang, 15810, IndonesiaDiabetes is one of the most serious health challenges in both developed and developing countries. Early detection and accurate diagnosis of diabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease has gradually increased. A promising classification technique in machine learning is the use of support vector machines in combination with radial basis function kernels (SVM-RBF). In this study, we used SVM-RBF to predict diabetes. The study used a Pima Indian diabetes dataset from the University of California, Irvine (UCI) Machine Learning Repository. The subjects were female and ? 21 years of age at the time of the index examination. Our experiment design used 10-fold cross-validation. Confusion matrix and ROC were used to calculate performance evaluation. Based on the experimental results, the study demonstrated that SVM-RBF shows promise in aiding diagnosis of Pima Indian diabetes disease in the early stage.http://ijtech.eng.ui.ac.id/article/view/1893DiabetesPima datasetSVM-RBF |
| spellingShingle | Abdul Azis Abdillah Suwarno Suwarno Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels International Journal of Technology Diabetes Pima dataset SVM-RBF |
| title | Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels |
| title_full | Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels |
| title_fullStr | Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels |
| title_full_unstemmed | Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels |
| title_short | Diagnosis of Diabetes Using Support Vector Machines with Radial Basis Function Kernels |
| title_sort | diagnosis of diabetes using support vector machines with radial basis function kernels |
| topic | Diabetes Pima dataset SVM-RBF |
| url | http://ijtech.eng.ui.ac.id/article/view/1893 |
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