Abstract—The 21st century has been witnessing a high
growth in technology in every field including the medical sector.
Dynamic systems have been designed and implied for better and
accurate diagnosis of a large variety of ailments; but, the
growing number of patients makes it difficult to provide proper
medical attention in time. To overcome this difficulty, Intelligent
Systems techniques can be employed in the medical sector and
help us overcome the huge difference in the ratio of doctors
versus patients; along with reducing the examination and
waiting time for the patients. Among all the variety of ailments
prevailing in today’s world, “Lower Back Pain” has emerged as
one of the most prevailing ailments which includes around 80%
of the total population once in lifetime, making it to one of the
prior concerns of medical sector. To act effectively onto it, many
conventional methods have been used to diagnose lower back
pain. This study aims to design a non-Conventional technique to
classify Lower back pain either Normal or Abnormal using
Machine Learning techniques such as Naïve Bayes, Support
Vector Machines, Decision Trees, Gradient Boosted Trees, Fast
Large Margin, K Nearest Neighbor, Multilayer Perceptron,
Random Forest, and Artificial Neural Networks. This research
focuses upon the implementation of the above-mentioned
techniques for the proper classification of Spine Dataset and for
determining the best technique in terms of Accuracy, Precision,
Sensitivity, Specificity, F-measure and Area under Curve.
Index Terms—Machine learning, lower back pain, automatic
feature engineering technique. performance evaluation.
Shubham Sharma is with the University of Regina, Canada (e-mail:
ss235667@gmail.com).
Dr. Rene V. Mayorga is with the Faculty of Engineering and Applied
Science, University of Regina, Canada (e-mail:
Rene.Mayorga@uregina.ca).
Cite: Shubham Sharma and Rene V. Mayorga, "A Machine Learning Approach for the Classification of Lower Back Pain in the Human Body," International Journal of Machine Learning and Computing vol. 12, no. 5, pp. 229-235, 2022.
Copyright @ 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).