Understanding Low Back Pain Using Fuzzy Association Rule Mining

Low back pain (LBP) affects most people at some time in their life and psychological factors are often viewed as obstacles to recovery. LBP is often accompanied by hyperactivity of superficial Para spinal muscles and it has been suggested that psychological factors may affect the condition via increased spinal loading resulting from altered Para spinal muscle activity. Several measurements are taken, including physical factors (muscle activity, pain intensity, disability) and psychosocial factors (anxiety, depression, fear of movement etc) using several numerical scales and questionnaires. The aim of this work is to obtain relationships between these measurements. Most data recorded for LBP is numerical and range bound (intervalised or scaled), therefore presenting inherent fuzziness. To find relationships in the data, we have used a fuzzy association rule mining approach to identify correlations. Further, the use of fuzzy terms (linguistic terms) in the generated fuzzy rules helps to interpret the clinical outcome (readability). To show the applicability of this method, we have conducted experiments on a real LBP clinical dataset which indicate both valuable associations and understandable and interpretable rules. The results have been validated by an exercise and sports scientist.