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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Stroke
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1407014
This article is part of the Research Topic Artificial Intelligence in Acute Neurology View all 13 articles

Predicting the Recurrence of Spontaneous Intracerebral Hemorrhage Using a Machine Learning Model

Provisionally accepted
Chaohua Cui Chaohua Cui *Jiaona Lan Jiaona Lan Zhenxian Lao Zhenxian Lao Tianyu Xia Tianyu Xia Tonghua Long Tonghua Long
  • Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangx, China

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

    Background: Recurrence can worsen conditions and increase mortality in ICH patients.Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes potentially. A machine learning model with improved performance is necessary to predict recurrence. Methods: We collected data from ICH patients in two hospitals for our retrospective training cohort and prospective testing cohort. The outcome was the recurrence within one year. We constructed logistic regression, support vector machine (SVM), decision trees, Voting Classifier, random forest, and XGBoost models for prediction. Results: The model included age, NIHSS score at discharge, hematoma volume at admission and discharge, PLT, AST, and CRP levels at admission, use of hypotensive drugs and history of stroke. In internal validation, logistic regression demonstrated an AUC of 0.89 and precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achieved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and precision of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, and XGBoost recorded an AUC of 0.93 and precision of 0.91. Conclusion: The machine learning models performed better in predicting ICH recurrence than traditional statistical models. The XGBoost model demonstrated the best comprehensive performance for predicting ICH recurrence in the external testing cohort.

    Keywords: intracerebral hemorrhage, Recurrence, Predicting, Model, machine learning intracerebral hemorrhage, machine learning

    Received: 26 Mar 2024; Accepted: 02 May 2024.

    Copyright: © 2024 Cui, Lan, Lao, Xia and Long. 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: Chaohua Cui, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangx, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.