Abstract
OBJECTIVE: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system. METHODS: A total of 303 patients with intracranial aneurysms treated with endovascular intervention at four hospitals (FuShun County Zigong City People's Hospital, Nanchong Central Hospital, The Third People's Hospital of Yibin, The Sixth People's Hospital of Yibin) from January 2022 to September 2023 were selected. These patients were divided into a good prognosis group (n = 207) and a poor prognosis group (n = 96). An improved machine learning model was employed to analyze patient clinical data, aiding in the construction of a prediction model for poor prognosis in intracranial aneurysm endovascular intervention. This model simultaneously performed feature selection and weight determination. Logistic multivariate analysis was used to validate the selected features. Additionally, a visualization system was developed to automatically calculate the risk level of poor prognosis. RESULTS: In the training set, the improved machine learning model achieved a maximum F1 score of 0.8633 and an area under the curve (AUC) of 0.9118. In the test set, the maximum F1 score was 0.7500, and the AUC was 0.8684. The model identified 10 key variables: age, hypertension, preoperative aneurysm rupture, Hunt-Hess grading, Fisher score, ASA grading, number of aneurysms, intraoperative use of etomidate, intubation upon leaving the operating room, and surgical time. These variables were consistent with the results of logistic multivariate analysis. CONCLUSIONS: The application of improved machine learning models for the analysis of patient clinical data can effectively predict the risk of poor prognosis following endovascular intervention for intracranial aneurysms at an early stage. This approach can assist in formulating intervention plans and ultimately improve patient outcomes.