Machine Learning Algorithms for Clinical Diagnosis of Lower Back Pain – A Survey

Abstract

            Lower back pain due to spinal abnormality causes severe disability in 80% of all age population globally. The primary cause of lower back pain is injuries in bones, joints, ligaments, muscles and spinal cord, leading to spinal abnormalities. LBP may also occur in persons with lifestyle changes, improper sitting position, sitting within the same position for a longer time cause an uncomfortable condition in the spine. However, it is often difficult to discriminate the intensity of pain to an abnormal condition. The advancement in artificial intelligence and machine learning techniques helped diagnose low back pain by analysing the clinical diagnostic information of a patient. An appropriate feature selection technique is used to identify significant features for the classification process. The severity of the spinal disorder or abnormalities in lower back pain patients can be diagnosed with Machine learning algorithms. These algorithms are used to classify spinal abnormality in a dataset with 310 patients retrieved from Kaggle repository. This article mainly presents various literary works that have used machine learning algorithms to classify lower back pain in patients with spinal abnormalities.