A Comparison of C4.5 and K-Nearest Neighbor Algorithm on Classification of Disk Hernia and Spondylolisthesis in Vertebral Column

Irma Handayani, Suprapto Suprapto

Abstract


Good spinal health is needed to carry out daily activities. Trauma to the vertebral column can affect the spinal cord's ability to send and receive messages from the brain to the body's sensory and motor control systems. Disk hernia and spondylolisthesis are examples of pathology of the vertebral column. Research on pathology or damage to bones and joints of the skeletal system is rare. Whereas the classification system can be used by radiologists as a "second opinion" so that it can improve productivity and diagnosis consistency from that radiologist. This study compared the accuracy values of the C4.5 and K-NN algorithms in the classification of herniated disc disease and spondylolisthesis as well as a comparison of the speed of time in the classification process. Tests were carried out using data from 310 patients with normal conditions (100 patients), herniated disks (60 patients), and spondylolisthesis (150 patients). The results showed that the accuracy of the C4.5 classifier was 89% and the K-NN classifier was 83%. The average time needed to classify the C4.5 classifier is 0.00912297 seconds and the K-NN classifier is 0.000212303 seconds.


Keywords


Vertebral Column; C4.5 Algorithm; K-NN Algorithm; Disk Hernia; Spondylolisthesis

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DOI: https://doi.org/10.30865/mib.v7i3.6394

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