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Predictive Modeling of Comorbid Anxiety in Young Hypertensive Patients Based on a Machine Learning Approach
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作者 Haiyan Xiao aide fan +1 位作者 Zhiyong Liu Keping Yang 《Journal of Clinical and Nursing Research》 2025年第4期130-136,共7页
Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content... Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models. 展开更多
关键词 Machine learning method Youth hypertension ANXIETY Prediction model
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风险量化评估助力社区矫正高质量发展路径探析
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作者 范爱德 刘熙 《社区矫正理论与实践》 2024年第4期21-26,共6页
面对社区矫正对象数量激增、工作难度大、人员配备不足等困难和问题,黔东南州司法局深入研究社区矫正对象再犯罪预防和风险量化评估工作,建设了社区矫正安全稳定平台,帮助社区矫正工作人员科学、精准、便捷地找出再犯罪风险高的社区矫... 面对社区矫正对象数量激增、工作难度大、人员配备不足等困难和问题,黔东南州司法局深入研究社区矫正对象再犯罪预防和风险量化评估工作,建设了社区矫正安全稳定平台,帮助社区矫正工作人员科学、精准、便捷地找出再犯罪风险高的社区矫正对象,对照每名社区矫正对象的风险点有针对性地化解安全隐患。 展开更多
关键词 社区矫正对象 风险评估 个别化矫正
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