Diagnosis of diabetes using support vector machines with radial basis function kernels
Abdul Azis Abdillah;
Suwarno
([Publisher not identified]
, 2016)
|
| Diabetes is one of themost serious health challenges in both developed and developing countries. Early detection and accurate diagnosis ofdiabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease hasgradually increased. A promising classification technique in machinelearning is the use of support vector machines in combination with radial basisfunction kernels (SVM-RBF). In this study, we used SVM-RBF to predict diabetes.The study used a Pima Indian diabetes dataset from the University ofCalifornia, Irvine (UCI) Machine Learning Repository. The subjects were female and ≥ 21 yearsof age at the time of the index examination. Our experiment design used 10-foldcross-validation. Confusion matrix and ROC were used to calculate performanceevaluation. Based on the experimental results, the study demonstrated that SVM-RBFshows promise in aiding diagnosis of Pima Indian diabetes disease in the earlystage. |
| No. Panggil : | J-Pdf |
| Entri utama-Nama orang : | |
| Entri tambahan-Nama orang : | |
| Subjek : | |
| Penerbitan : | [Place of publication not identified]: [Publisher not identified], 2016 |
| Sumber Pengatalogan : | LibUI eng rda |
| ISSN : | 20872100 |
| Majalah/Jurnal : | International Journal of Technology (IJTECH) |
| Volume : | Vol 7, No 5 (2016) 849-858 |
| Tipe Konten : | text |
| Tipe Media : | computer |
| Tipe Carrier : | online resource |
| Akses Elektronik : | http://www.ijtech.eng.ui.ac.id/index.php/journal/article/view/1370 |
| Institusi Pemilik : | Universitas Indonesia |
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| No. Panggil | No. Barkod | Ketersediaan |
|---|---|---|
| J-Pdf | 03-17-355520186 | TERSEDIA |
| Ulasan: |
| Tidak ada ulasan pada koleksi ini: 20449802 |
