A prognostic model for interventional thrombectomy in patients with acute ischemic stroke based on a BP neural network, random forest model and decision tree model.

Objective: To investigate the predictive effect of a Back propagation (BP) neural network model, a random forest (RF) model and a decision tree model on the prognosis of interventional thrombolectomy for acute ischemic stroke (AIS) patients.

Methods: A total of 255 patients with AIS admitted to the Department of Neurology, Beiliu People's Hospital of Guangxi from March 2018 to February 2022 were retrospectively included, all of whom received interventional thromposectomy. Patients' prognosis was determined by the modified Rankin Scale (mRs) at 3 months after surgery, including the good prognosis group (mRs≤2 points) and the poor prognosis group (mRs 3-6 points).