Name
Title | ||
|---|---|---|
A rule extraction approach from support vector machines for diagnosing hypertension among diabetics |
Abstract | ||
|---|---|---|
•Classification of datasets on diabetes and its complications are considered.•Five feature selection algorithms are utilized for choosing significant features.•A hybrid rule-extraction method generating comprehensible rule sets is developed.•Experiments were performed on six datasets: one new and five public.•The proposed approach outperforms ten state-of-the-art classifiers. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2019.04.029 | Expert Systems with Applications |
Keywords | Field | DocType |
Diabetes,Extreme gradient boosting,Hypertension,Medical diagnosis,Rule extraction,Support vector machine | Public health,Early detection,Data mining,Diabetes mellitus,Disease,Feature selection,Computer science,Support vector machine,Artificial intelligence,Black box,Statistical classification,Machine learning | Journal |
Volume | ISSN | Citations |
130 | 0957-4174 | 2 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Namrata Singh | 1 | 2 | 0.70 |
| Pradeep Singh | 2 | 17 | 5.62 |
| Deepika Bhagat | 3 | 2 | 0.36 |