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- Title
Prediction Model for unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning.
- Authors
Shengli Li; Jianan Zhang; Xiaoqun Hou; Yongyi Wang; Tong Li; Zhiming Xu; Feng Chen; Yong Zhou; Weimin Wang; Mingxing Liu
- Abstract
Objective: The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods: Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR).
- Subjects
CEREBRAL hemorrhage; MACHINE learning; RECEIVER operating characteristic curves; PREDICTION models; SUPPORT vector machines
- Publication
Journal of Korean Neurosurgical Society, 2024, Vol 67, Issue 1, p94
- ISSN
2005-3711
- Publication type
Article
- DOI
10.3340/jkns.2023.0118