Prediction and Feature Analysis of Intracranial Aneurysms in Community Residents: A Study Based on Machine Learning.
The global incidence of intracranial aneurysms is increasing annually, and their rupture is associated with a high mortality rate. Many community residents often unknowingly develop intracranial aneurysms and are at risk of rupturing. To solve this problem, we conduct an innovative approach using ma...
| Publicado en: | Health & Social Care in the Community Vol. 2025; pp. 1 - 19 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/30/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187636400&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187636400 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09660410 EVX jtl: Health & Social Care in the Community issn: 09660410 maglogo: Y pubinfo: dt: 8/30/2025 vid: 2025 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 187636400 187636400 187636400 10.1155/hsc/3585981 187636400 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Prediction and Feature Analysis of Intracranial Aneurysms in Community Residents: A Study Based on Machine Learning. aug: au: Wang, Xinwei Wang, Sutong Wang, Dujuan Sima, Xiutian Basu Roy, Sohini affil: Business School,, Sichuan University,, Chengdu, 610064,, China, scu.edu.cn sug: subj: Prediction Models Machine Learning Cerebral Aneurysm Epidemiology Cerebral Aneurysm Complications Brain Diseases Etiology Community Living Decision Support Techniques Conceptual Framework World Health Human Funding Source China Male Female Middle Age Aged Retrospective Design Prospective Studies Hospitals, Public Descriptive Statistics Convolutional Neural Networks Deep Learning Sensitivity and Specificity Ensemble Learning Middle Aged: 45-64 years Aged: 65+ years Male Female ab: The global incidence of intracranial aneurysms is increasing annually, and their rupture is associated with a high mortality rate. Many community residents often unknowingly develop intracranial aneurysms and are at risk of rupturing. To solve this problem, we conduct an innovative approach using machine learning to predict both the occurrence and rupture of intracranial aneurysms in patients with brain diseases and analyze the essential features derived from residents' health data at various stages of clinical admission. Specifically, we design an ensemble classifier candidate pool model for the initial two stages of admission diagnosis and a deep fusion network model that integrates textual and structured data for the detailed screening stage. Also, the feature importance is explored by the Shapley value and word frequency. The proposed deep fusion neural network achieves the highest predictive performance, with a precision of 0.787, sensitivity of 0.785, specificity of 0.870, F1 score of 0.785, and AUC of 0.871. In addition, text features contribute most significantly to model output, and word frequency varies across different disease types in patient medical records. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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