Feature selection and classification in supporting report-based self-management for people with chronic pain

Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting ( the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for people to complete at home. This paper presents a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self-management. In addition, a classification model is proposed to differentiate the treatment stages. Four different feature selection methods were applied to rank the questions. In addition, four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance. There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or AUC (p > 0.05); however, there were significant differences between the classifiers for each ranking method (p < 0.001). The results showed the multilayer perceptron classifier had the best classification performance on an optimized subset of questions, which consisted of ten questions. Its overall classification accuracy and AUC were 100% and 1, respectively.