Classification of Spinal Muscle Atrophy Disease using SVM in Machine Learning
B. Ganga Bhavani1, G. L. N. V. S. Kumar2, M. L. Rekha3, B. P. N. Madhu Kumar4, Raja Rao P. B. V.5
1Billa Ganga Bhavani, Department Of CSE, BVC Engineering College, Odalarevu, JNTUK Kakinada, AP, India.
2G. L. N. V. S. Kumar, Department of MCA, BVCITS, Amalapuram, JNTUK Kakinada, AP, India.
3M. Lakshmi Rekha, Department of MCA, BVCITS, Amalapuram, JNTUK Kakinada, AP, India.
4B. P. N. Madhu Kumar, Department Of CSE,BVC Engineering College, Odalarevu, JNTUK Kakinada, AP, India.
5Raja Rao P. B. V., Department Of CSE, BVC Engineering College, Odalarevu, JNTUK Kakinada, AP, India.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1807-1811 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2568129219/2019©BEIESP | DOI: 10.35940/ijeat.B2568.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: SMA is a genetic neuromuscular disease. It is a rare disease. It is caused by mutations in the survival motorneuron (SMN) gene that encodes SMN Protein. Maindifficult area of SMA is muscle weakness, causing withdifficulty with moving, swallowing or breathing. Thereare four types of SMA’s. The primary objective of thispaper is to classify the SMA’s by using support vectormachine classifier. Then we can easily predict the life span of the children based on the group of SMA. This disease is classified on the basis of age of onset and clinical course.
Keywords: SMN1, SMN2, SVM, SVC, CPK, SMA Linear, RBF, Polynomial.