Stroke Risk Prediction and Assessment Based on Big Aata Analysis
DOI:
https://doi.org/10.62177/apjcmr.v1i1.196Keywords:
Stroke, BP Neural Network, Genetic Algorithm, Support Vector Machine, Random Forest, Risk Prediction EvaluationAbstract
Stroke is a common cardiovascular and cerebrovascular disease with high morbidity, high mortality and high disability rate. In this paper, a stroke risk prediction and evaluation model based on support vector machine, random forest, BP neural network and genetic algorithm optimization neural network algorithm was established by using a raw dataset including 10 characteristic variables such as gender, age, hypertension, heart disease, and 1 stroke target variable. The experimental results show that the average blood glucose level, body mass index, hypertension and other variables have a great impact on the risk of stroke, and the neural network algorithm optimized by the genetic algorithm performs slightly better than the other three models.
Downloads
References
Xu Xiao, Zhang Lei, Wang Zhen. Brain Stroke Risk Prediction System Based on Fuzzy Comprehensive Evaluation Method[J]. Computer Simulation, 2015, 32(07): 344-347 + 360.
Lai Xinxing. Study on Risk Factors and Prediction Model of Early Neurological Deterioration in Acute Ischemic Stroke[D]. Beijing University of Chinese Medicine, 2017.
Wu Juhua, Zhang Shuo, Tao Lei, et al. Research on Stroke Risk Prediction Model Based on Neural Network[J]. Data Analysis and Knowledge Discovery, 2019, 3(12): 70-75.
Luo Yishu, Shao Yuanyuan, Chen Dehua. Ischemic Stroke Diagnosis Model Based on the Combination of Multiple Features of LSTM[J]. Intelligent Computer and Its Applications, 2020, 10(10): 74-79.
Hou Yumei, Zeng Hui, Zhang Chenyang, et al. Prediction of the Risk of Ischemic Stroke Based on Data Mining[J]. Chinese Journal of Gerontology, 2021, 41(01): 177-181.
Yang Huijie, Dong Jiankai, Hu Quanzhong. Study on the Relationship between the Ratio of Serum C-reactive Protein to Albumin and Carotid Artery Stenosis in Patients with Acute Cerebral Infarction[J]. International Journal of Laboratory Medicine, 2023, 44(08): 956-960.
Thomas, Lumley, and, et al.A stroke prediction score in the elderly: validation and Web-based application[J].Journal of Clinical Epidemiology, 2002.DOI:10.1016/S0895-4356(01)00434-6. DOI: https://doi.org/10.1016/S0895-4356(01)00434-6
Manuel D G, Meltem T, Richard P, et al.Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT)[J].Plos One, 2015, 10(12):e0143342.DOI:10.1371/journal.pone.0143342. DOI: https://doi.org/10.1371/journal.pone.0143342
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2025 Menghan Gao, Jiayin Chen, Hongyan Gao
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DATE
Accepted: 2025-03-21
Published: 2025-03-24
