Author(s): Sushmita Lenka, Nancy Victor

Email(s): nancyvictor@vit.ac.in

DOI: 10.52711/0974-360X.2022.00262   

Address: Sushmita Lenka1, Nancy Victor2*
1FICO - Solution Integration - Consultant, Bangalore, India.
2Assistant Professor, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
*Corresponding Author

Published In:   Volume - 15,      Issue - 4,     Year - 2022


ABSTRACT:
Back pain is one of the most popular diseases which cause extreme discomfort for patients. More than 80% of the people’s day to day activities are affected due to lower back pain. The symptom sometimes gets neglected and worsens the situation, which can cause lifelong damage to vital organs. Lower back pain can be classified as normal and abnormal LBP based on the boundary values of various parameters. Extensive research has been carried out in this field and most of the classification techniques serve the purpose by classifying the data with already provided accuracy values. However, this paper provides a novel technique by adding feature parameter tuning which acts as a catalyst in increasing the accuracy and thereby identifying the effective parameters that help in the optimization.


Cite this article:
Sushmita Lenka, Nancy Victor. Lower Back Pain Classification using Parameter Tuning. Research Journal of Pharmacy and Technology. 2022; 15(4):1573-8. doi: 10.52711/0974-360X.2022.00262

Cite(Electronic):
Sushmita Lenka, Nancy Victor. Lower Back Pain Classification using Parameter Tuning. Research Journal of Pharmacy and Technology. 2022; 15(4):1573-8. doi: 10.52711/0974-360X.2022.00262   Available on: https://rjptonline.org/AbstractView.aspx?PID=2022-15-4-28


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