Research on optimal Traditional Chinese Medicine treatment of knee ostarthritis with data mining algorithms

Abstract

At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.