首页|First Affiliated Hospital of Xinjiang Medical University Reports Findings in Str oke (Machine learning approaches to identify the link between heavy metal exposu re and ischemic stroke using the US NHANES data from 2003 to 2018)
First Affiliated Hospital of Xinjiang Medical University Reports Findings in Str oke (Machine learning approaches to identify the link between heavy metal exposu re and ischemic stroke using the US NHANES data from 2003 to 2018)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cerebrovascular Diseas es and Conditions - Stroke is the subject of a report. According to news reporti ng originating in Xinjiang, People’s Republic of China, by NewsRx journalists, r esearch stated, “There is limited understanding of the link between exposure to heavy metals and ischemic stroke (IS). This research aimed to develop efficient and interpretable machine learning (ML) models to associate the relationship bet ween exposure to heavy metals and IS.” The news reporters obtained a quote from the research from the First Affiliated Hospital of Xinjiang Medical University, “The data of this research were obtaine d from the National Health and Nutrition Examination Survey (US NHANES, 2003-201 8) database. Seven ML models were used to identify IS caused by exposure to heav y metals. To assess the strength of the models, we employed 10-fold crossvalida tion, the area under the curve (AUC), F1 scores, Brier scores, Matthews correlat ion coefficient (MCC), precision-recall (PR) curves, and decision curve analysis (DCA) curves. Following these tests, the best-performing model was selected. Fi nally, the DALEX package was used for feature explanation and decision-making vi sualization. A total of 15,575 participants were involved in this study. The bes t-performing ML models, which included logistic regression (LR) (AUC: 0.796) and XGBoost (AUC: 0.789), were selected. The DALEX package revealed that age, total mercury in blood, poverty-to-income ratio (PIR), and cadmium were the most sign ificant contributors to IS in the logistic regression and XGBoost models.”
XinjiangPeople’s Republic of ChinaAs iaCerebrovascular Diseases and ConditionsCyborgsEmerging TechnologiesHea lth and MedicineMachine LearningStroke
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