Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm

This project conducts a one-year follow-up on the enrolled subjects (patients with ischemic stroke) to observe the recurrence and modified Rankin scores after discharge. Combining complex multidimensional data such as demographic information, physical examination, medical history, imaging images, medication status, NIHSS scale scores, Glasgow Coma Scale scores, CYP2C19 genotype test results, and laboratory examinations of the subjects; Convolutional Neural Networks (CNNs) are used to segment lesions and extract features from the subjects' imaging images; Cox regression models are employed to obtain factors influencing recurrence; a prediction model for the risk of recurrence within 1, 3, 6, and 12 months for ischemic stroke is constructed using the XGBoost algorithm combined with Convolutional Neural Networks. The exploration aims to provide new insights and methods for the prevention and control of major chronic diseases by evaluating the effectiveness of XGBoost in predicting the risk of ischemic stroke recurrence at different times.

1. Inclusion Criteria：

• 18-85 years old;
• Diagnosed with ischemic stroke or transient ischemic attack (diagnosis meets the criteria established by the Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association in 2014);
• During the acute phase of onset (2 weeks);
• Voluntarily participate and sign an informed consent form.

2. Exclusion criteria：

• Cancer patients;
• Cardiogenic infarction, cerebral infarction of other causes, and cerebral infarction of unknown causes;
• Patients with hemorrhagic stroke, mixed stroke, and tumor stroke;
• Merge with severe heart, lung, and liver system diseases;
• Researchers identify patients with poor compliance and inability to complete long-term follow-up;
• Patients currently participating in any clinical trials related to investigational drugs or medical devices.