Study on the intelligent evaluation model after stoke based on random forest

Abstract

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.