Handayani, Irma (2019) Peer Riview: APPLICATION OF K-NEAREST NEIGHBOR ALGORITHM ON CLASSIFICATION OF DISK HERNIA AND SPONDYLOLISTHESIS IN VERTEBRAL COLUMN. Universitas Atma Jaya Yogyakarta, https://ojs.uajy.ac.id/index.php/IJIS/article/view/2352/1422.
|
Text
Peer Review Irma Handayani.pdf Download (1MB) | Preview |
Abstract
Vertebral column as a part of backbone has important role in human body. Trauma in vertebral column can affect spinal cord capability to send and receive messages from brain to the body system that controls sensory and motoric movement. Disk hernia and spondylolisthesis are examples of pathologies on the vertebral column. Research about pathology or damage bones and joints of skeletal system classification is rare whereas the classification system can be used by radiologists as a second opinion so that can improve productivity and diagnosis consistency of the radiologists. This research used dataset Vertebral Column that has three classes (Disk Hernia, Spondylolisthesis and Normal) and instances in UCI Machine Learning. This research applied the K-NN algorithm for classification of disk hernia and spondylolisthesis in vertebral column. The data were then classified into two different but related classification tasks: “normal” and “abnormal”. K-NN algorithm adopts the approach of data classification by optimizing sample data that can be used as a reference for training data to produce vertebral column data classification based on the learning process. The results showed that the accuracy of K-NN classifier was 83%. The average length of time needed to classify the K-NN classifier was 0.000212303 seconds. Keywords: K-NN algorithm; disk hernia; spondylolisthesis; classification; vertebral column
| Item Type: | Other |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Teknik Komputer |
| Depositing User: | Mrs Irma Handayani |
| Date Deposited: | 06 Sep 2019 06:05 |
| Last Modified: | 07 Sep 2019 04:03 |
| URI: | http://eprints.uty.ac.id/id/eprint/3413 |
Actions (login required)
 |
View Item |
