Classification of Lower Back Pain Disorder Using Multiple Machine Learning Techniques and Identifying Degree of Importance of Each Parameter

Abstract

Lower back pain can occur due to various reasons involving any body part such as the interconnected network of spinal cord, nerves, bones, discs or tendons in the lumbar spine. Understanding and knowing about the origins of this disorder and getting a diagnostic treatment that determines the underlying reason, is the primary step in achieving an effective and efficient cure. Despite of heavy spending on resources such as time and money, devoted to lower back pain research methodologies, fruitful management remains a significant goal and lower back pain continues to be a cause of considerable concern on the primary care setting. One of the reason for this, could be the degrees of importance for researching into specific domain like these, are regularly developed by researchers and funding bodies, with less consideration of the needs of primary care practitioners. This study aims to determine the research priorities of primary care practitioners who manage low back pain on a day-today basis and to identify whether a person is abnormal or normal using collected physical spine data of 381 patients with 12 parameters. This study also identifies the degree of importance of each parameter used in the classification and ranks those parameters accordingly.