ArticleBMC medical informatics and decision making2019
Using machine learning models to improve stroke risk level classification methods of China national stroke screening.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- An explainable ensemble learning-based auxiliary diagnosis system for cerebral small vessel disease.Scientific reports · 2026Article
- Explainable machine learning for long-term cardiovascular disease risk prediction in Chinese middle-aged and older adults: a 9-year longitudinal cohort study with web-based risk calculator.Scientific reports · 2026Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Development of a gastric cancer risk calculator for questionnaire-based surveillance of Iranian dyspeptic patients.BMC gastroenterology · 2024Article
- SRPNet: stroke risk prediction based on two-level feature selection and deep fusion network.Frontiers in physiology · 2024Article
- Development of rapid and effective risk prediction models for stroke in the Chinese population: a cross-sectional study.BMJ open · 2023Article
- Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.International journal of telemedicine and applications · 2023Article
- RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data.PeerJ. Computer science · 2023Article
- Stroke Risk Prediction with Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
- Positive Effect of α-Asaronol on the Incidence of Post-Stroke Epilepsy for Rat with Cerebral Ischemia-Reperfusion Injury.Molecules (Basel, Switzerland) · 2022Article
- Interpretable CNN for ischemic stroke subtype classification with active model adaptation.BMC medical informatics and decision making · 2022Article
- Return-to-Work Predictions for Chinese Patients With Occupational Upper Extremity Injury: A Prospective Cohort Study.Frontiers in medicine · 2022Article
- A review of risk concepts and models for predicting the risk of primary stroke.Frontiers in neuroinformatics · 2022Review
- Machine learning models for screening carotid atherosclerosis in asymptomatic adults.Scientific reports · 2021Article
- Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.Sensors (Basel, Switzerland) · 2021Article
- Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.Frontiers in neurology · 2021Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency.
methodFirstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels.
resultThe recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year.
conclusionModels developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.
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