Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Abstract

Low-back pain (LBP) is a complex health problem requiring accurate diagnosis and effective treatment. However, the current decision support system (DSS) for LBP only considers the patient’s pain intensity and treatment suitability, which may not lead to optimal outcomes. This paper proposes a novel DSS that combines machine learning (ML) and expert input to classify LBP types and provide more reliable and personalized recommendations. We used an open-source dataset to train and test various ML models, including an ensemble model that combines multiple classifiers. We also performed data analysis and feature extraction to enhance the model’s predictive power. We developed a prototype tool to demonstrate the model’s performance and usability. Our results show that the ensemble model achieved the highest accuracy of 92.02%, followed by random forest (RF) (91.01%), multilayer perceptron (MP) (91.01%), and support vector machine (SVM) (87.88%). Our findings suggest that ML can help LBP specialists diagnose and treat LBP more effectively by learning from historical data and predicting LBP categories. Our DSS can potentially improve the quality of life for LBP patients and reduce the burden on the healthcare system.