Analysis of main risk factors causing stroke in Shanxi Province based on machine learning models

Liu, Junjie; Sun, Yiyang; Ma, Jing; Tu, Jiachen; Deng, Yuhui; He, Ping; Li, Rongshan; Hu, Fengyun; Huang, Huaxiong; Zhou, Xiaoshuang; Xu, Shixin

doi:10.1016/j.imu.2021.100712

Cited by 6 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Presently, the state-of-the-art models of machine learning in healthcare are based on tree models 20,28,29 and DNNs 15,16…”

Section: Model Comparisonmentioning

confidence: 99%

“…Based on the census data in both communities and hospitals from Shanxi Province, the authors in 20 proposed a model for three-risk-state (low/medium/high) prediction, and investigated different stroke risk factors, including the "8+2" main risk factors proposed by the China National Stroke Prevention Project (CSPP) and their ranking in Shanxi. Hypertension, physical inactivity (lack of sports), and obesity were ranked as the top three stroke risk factors in Shanxi.…”

Section: Introductionmentioning

confidence: 99%

“…For example, one patient is at a high-risk state, but abnormal glycosylated hemoglobin and other factors make it possible for the patient to transfer to an attack state. This finding prompts us to append the attack state to those considered in 20 , which occur before the attack.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Sun

Liu

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Stroke is the leading cause of death in China (Zhou et al. The Lancet 2019). A dataset from Shanxi Province is analyzed to predict the risk of patients’ at four states low/medium/high/attack) and to estimate transition probabilities between various states via a SHAP DeepExplainer. To handle the issues related to an imbalanced sample set, the quadratic interactive deep model (QIDeep) was first proposed by flexible selection and appending of quadratic interactive features. The experimental results showed that the QIDeep model with 3 interactive features achieved the state-of-the-art accuracy 83:33%(95% CI: (83:14%;83:52%)). Blood pressure, physical inactivity, smoking, weight and total cholesterol are the top five most important features. For the sake of high recall in the attack state, stroke occurrence prediction is considered an auxiliary objective in multi-objective learning. The prediction accuracy was improved, while the recall of the attack state was increased by 17:79% (to 82:06%) compared to QIDeep (from 71:49%) with the same features. The prediction model and analysis tool in this paper provided not only a prediction method but also an attribution explanation of the risk states and transition direction of each patient, a valuable tool for doctors to analyze and diagnose the disease.

show abstract

“…Presently, the state-of-the-art models of machine learning in healthcare are based on tree models 20,28,29 and DNNs 15,16…”

Section: Model Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Sun

Liu

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system [1] that disrupts body sensory and motoric systems [2]. This condition causes all body functions controlled by brain tissue to be disrupted.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, many classifiers from machine learning models have been used in some researches, especially on stroke disease. Research conducted by Liu [1] used a machine learning model called random forest in classifying cause factors of stroke disease, resulting in 85.03% accuracy. Another research was conducted by Zhu [13] identified stroke ischemic onset time based on DWI and FLAIR imaging with Convolutional Neural Network (CNN) model, yielding an accuracy of 80.50%.…”

Section: Introductionmentioning

confidence: 99%

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Purwono

Ma’arif

Negara

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

View full text Add to dashboard Cite

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.

show abstract

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Sun

Liu

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Stroke is the leading cause of death in China (Zhou et al. in The Lancet, 2019). A dataset from Shanxi Province is analyzed to predict the risk of patients at four states (low/medium/high/attack) and to estimate transition probabilities between various states via a SHAP DeepExplainer. To handle the issues related to an imbalanced sample set, the quadratic interactive deep model (QIDeep) was first proposed by flexible selection and appending of quadratic interactive features. The experimental results showed that the QIDeep model with 3 interactive features achieved the state-of-the-art accuracy 83.33%(95% CI (83.14%; 83.52%)). Blood pressure, physical inactivity, smoking, weight, and total cholesterol are the top five most important features. For the sake of high recall in the attack state, stroke occurrence prediction is considered an auxiliary objective in multi-objective learning. The prediction accuracy was improved, while the recall of the attack state was increased by 17.79% (to 82.06%) compared to QIDeep (from 71.49%) with the same features. The prediction model and analysis tool in this paper provided not only a prediction method but also an attribution explanation of the risk states and transition direction of each patient, a valuable tool for doctors to analyze and diagnose the disease.

show abstract

Analysis of main risk factors causing stroke in Shanxi Province based on machine learning models

Cited by 6 publications

References 24 publications

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Contact Info

Product

Resources

About