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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Stroke
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1482119
This article is part of the Research Topic Intracranial aneurysms, AVM and other vascular malformations, and connective tissue disorders as potential causes of stroke: Advances in diagnosis and therapeutics including novel neurosurgical techniques View all 13 articles

Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model Chunyu LEI

Provisionally accepted
Chunyu Lei Chunyu Lei 1Anhui Fu Anhui Fu 2Bin Li Bin Li 3Shengfu Zhou Shengfu Zhou 4Jun Liu Jun Liu 3Yu Cao Yu Cao 1Bo Zhou Bo Zhou 3*
  • 1 Fushun County People's Hospital, Fushun, Sichuan, China
  • 2 Nanchong Central Hospital, Nanchong, Sichuan Province, China
  • 3 Yibin Third People's Hospital, Yibin, Sichuan Province, China
  • 4 The Sixth People's Hospital of Yibin, Yibin, China

The final, formatted version of the article will be published soon.

    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 ten 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.

    Keywords: Improve machine learning models, Intracranial Aneurysm, Intravascular intervention therapy, Poor prognosis, Visualization system Introduction

    Received: 17 Aug 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 Lei, Fu, Li, Zhou, Liu, Cao and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Bo Zhou, Yibin Third People's Hospital, Yibin, 644002, Sichuan Province, China

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