A clinical outcome evaluation model with local sample selection: A study on efficacy of acupuncture for cervical spondylosis

Local learning is a special learning framework that considers training samples located in a small region concentric of the query sample. In many applications the concept label of query sample can be evaluated effectively only by similar training samples, such as the famous K-nearest neighbors (KNN) classifier. The metric of locality or similarity is essential in local learning, which is often application oriented and implied in local geometry of input space. In this paper, we propose to apply local learning to the task of outcome assessment and evaluation on acupuncture for cervical spondylosis (CS) in a multi-center clinical trial. The analytic data are measures of three questionnaires which are recognized tools for subjective patient-reported outcomes (PROs) evaluation. We propose a similarity evaluation method based on both Euclidean distance and the therapy effect of recent records. A Non-Dominated Sort (NDS) based methods is applied to obtain a ranking of therapy effect. A WEKA implementation decision tree classifier is applied as the main learner in our work, with comparison to two base line methods. The result shows that the proposed local learning method dramatically outperforms the global version in both classification accuracy and computational costs.