JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Gupta, Utsav | |
| dc.contributor.author | Valarmathi, B. | |
| dc.contributor.author | Santhi, K. | |
| dc.contributor.author | Yahya, Abid | |
| dc.date.accessioned | 2020-08-17T14:48:50Z | |
| dc.date.available | 2020-08-17T14:48:50Z | |
| dc.date.issued | 2019-06 | |
| dc.identifier.citation | Gupta, U. et al (2019) Classification of patients data of predict signs of diabetic retinopathy. In Jamisola, Rodrigo S. Jr (ed.) BIUST Research and Innovation Symposium 2019 (RDAIS 2019); 1 (1) 71-75. | en_US |
| dc.identifier.issn | 2521-2292 | |
| dc.identifier.uri | http://repository.biust.ac.bw/handle/123456789/163 | |
| dc.description.abstract | In healthcare, data mining is fetching increasingly common, if not gradually essential. Data mining submissions can greatly profit all parties involved in the healthcare commerce. For instance, data mining can help healthcare guarantors notice fraud and exploitation, healthcare officialdoms make purchaser connection administration decisions, doctors recognise effective conducts and best performs, and patients take better and more affordable healthcare amenities. So using the data mining tools only we are here going to predict the efficient algorithms for detecting the certainty of Diabetic Retinopathy on the basis of various vector form of the data from patients images. Diabetic Retinopathy is one of the major causes of blindness in the people at younger age. The support vector machine algorithm (SVM) algorithm is the most efficient classifier for dataset contains features extracted from the Messidor image set, since it produces the highest accuracy value of 71.38% than Decision Tree and Random Forest algorithms. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Botswana International University of Science and Technology ( BIUST) | en_US |
| dc.subject | Data mining | en_US |
| dc.subject | Healthcare | en_US |
| dc.subject | Diabetic retinopathy | en_US |
| dc.subject | Blindness | en_US |
| dc.subject | Random forest | en_US |
| dc.subject | Decision tree | en_US |
| dc.subject | SVM | en_US |
| dc.title | Classification of patients data of predict signs of diabetic retinopathy | en_US |
| dc.description.level | phd | en_US |
| dc.description.accessibility | unrestricted | en_US |
| dc.description.department | cte | en_US |
Files in this item
This item appears in the following Collection(s)
-
BIUST Teaching, Research, and Innovation Symposium (TRDAIS)
The collection is made up of BIUST Teaching, Research, and Innovation Symposium (TRDAIS) paper presentations.).