Objective To analyze the factors influencing the therapeutic effect of"Shoulder Tri-needles therapy"in the treatment of shoulder-hand syndrome of stroke patients by using machine learning approach

so as to provide a feasibility for improving clinical efficacy.Methods A total of 586 stroke patients with shoulder-hand syndrome eligible for this study were involved in our machine learning experiments for classification of the influential factors.Their data including the age

gender

pulse condition

complexion

tongue quality

tongue coating

disease stage

body mass index(BMI)

blood pressure

blood glucose

blood triglyceride

blood total cholesterol

smoking history

drinking history

and final outcomes were extracted from the medical record system(from Oct.of 2014 to Jan.of 2017 in the First Affiliated Hospital and Shenzhen Futian Hospital of Guangzhou University of Chinese Medicine).The single rule algorithm(1 R)was adopted to learn

followed by optimization with Repeated Incremental Pruning to Produce Error Reduction(RIPPER)algorithm

and C 5.0 decision tree algorithm.Results The accurate classification rates of 1 R

RIPPER and decision tree model were 87.37%(512/586)

95.90%(562/586)

and 97.10%(569/586)

respectively.The final outcomes of machine learning of this study showed that the disease stage(acute or recovery stage)

complexion difference

tongue coating difference

blood pressure level

consumption of alcohol

BMI

and smoking habit were the most important factors influencing the therapeutic effect of"Shoulder Tri-needles"in the treatment of shoulder-hand syndrome of stroke patients.Conclusion The disease stage

complexion and tongue identification

blood pressure level

alcohol drinking and smoking habits

and BMI are the principal factors affecting the therapeutic effect of"Shoulder Tri-needles therapy"in the treatment of shoulder-hand syndrome of stroke patients.