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Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach

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Abstract

In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus inpatients admitted to Yueyang People’s Hospital from May 2019 to October 2022, of which 95 cases of hydrocephalus occurred after ICH and no hydrocephalus in 749 cases. The following indicators were compared between the two groups of patients: gender, age, Glasgow Coma Scale(GCS)score, whether the amount of bleeding was greater than 30 ml, whether it broke into the ventricle or not, modified Graeb score(MGS), modified Rankin Scale (MRS) score, whether surgery was performed or not, red blood cells, white blood cells, and platelets. After variable screening, the following six variables were selected: GCS score, MGS, MRS score, whether the bleeding volume was greater than 30 ml, whether it broke into the ventricle or not, and whether surgery was performed or not were modelled and analysed using logistic regression model and support vector machine model in machine learning. The results showed that under the same conditions, the accuracy of the support vector machine model was 0.89 and F1 was 0.838 ,the value of the AUC of the support vector machine model is 0.888; the accuracy of the logistic regression model was 0.902 and F1 was 0.89, the value of the AUC of the support vector machine model is 0.903. Compared with the group without hydrocephalus, patients in the group with hydrocephalus had bleeding volume greater than 30 ml, haemorrhage into the ventricles of the brain, and had undergone surgery in the brain, and the difference was statistically significant (P 0.001). Statistical analysis showed that GCS score ≤ 8.8, modified Graeb score (MGS) ≥ 10 and MRS score ≥ 3 were independent risk factors for the development of hydrocephalus after spontaneous ventricular haemorrhage. Therefore, patients with lower GCS score, higher modified Graeb score, higher MRS score, bleeding volume > 30 ml, haemorrhage into the ventricles of the brain, and experience of having undergone surgery in the brain should be operated on early to remove the intraventricular haematoma in order to reduce the incidence of hydrocephalus.
Title
Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach
Authors
Enwen Zhu
Zhuojun Zou
Jianxian Li
Jipan Chen
Ao Chen
Naifei Zhao
Qiang Yuan
Caicai Liu
Xin Tang
Publication date
01-01-2025
Publisher
Springer US
Published in
Neuroinformatics / Issue 1/2025
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-024-09710-5
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