Open AccessStudy on the intelligent evaluation model after stoke based on random forest
Author(s) -
Yaru Ge
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032138
Subject(s) - random forest , decision tree , stroke (engine) , computer science , rehabilitation , function (biology) , data collection , artificial intelligence , point (geometry) , physical medicine and rehabilitation , machine learning , medicine , statistics , physical therapy , mathematics , engineering , mechanical engineering , evolutionary biology , biology , geometry
According to the summary of “China cardiovascular report 2018”, in recent years, there are 2 million new stroke patients in China every year, and nearly 70% of them have lost hand function due to stroke. Therefore, the rehabilitation evaluation of postoperative hand function of stroke patients is complicated with low intelligence. In this paper, data collection methods are introduced firstly, and the original data are artificially classified. Then, starting from the decision tree method, the motion data of stroke patients are intelligently evaluated by establishing a random forest, and the evaluation results are returned to the patients for review in the form of floating point number based on their probability proportion. Finally, the results of the model experiment and its advantages and disadvantages are analyzed, and the future development direction and prospect are put forward.