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ArticleNeuroinformatics2025

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

Enwen Zhu, Zhuojun Zou, Jianxian Li, Jipan Chen, Ao Chen, Naifei Zhao, Qiang Yuan, Caicai Liu, Xin Tang

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Article in Neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Enwen ZhuDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
Zhuojun ZouDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
Jianxian LiDepartment of Neurosurgery, Yueyang People's Hospital, Yueyang, 414000, China.
Jipan ChenDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China. chenjipan2022@163.com.
Ao ChenDepartment of Neurosurgery, Yueyang People's Hospital, Yueyang, 414000, China.
Naifei ZhaoDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
Qiang YuanDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
Caicai LiuDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
Xin TangDepartment of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.

Funding

Graduate Research Innovation Project of Hunan Province CX20220952National Natural Science Foundation of China 52338009National Science Fund for Distinguished Young Scholars 52025085nnovation and entrepreneurship training program for college students S202310536043
6 · The paper itself

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.

Indexed as

Cerebral HemorrhageHydrocephalusMachine LearningAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesSupport Vector MachineHydrocephalusIntercerebral hemorrhageLogistic regressionMachine learningSupport vector machine

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