PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to investigate the prevalence of carotid atherosclerosis (CAS), especially among seniors, and develop a precise risk assessment tool to facilitate screening and early intervention for high-risk individuals.
UNASSIGNED: A comprehensive approach was employed, integrating traditional epidemiological methods with advanced machine learning techniques, including support vector machines, XGBoost, decision trees, random forests, and logistic regression.
UNASSIGNED: Among 1,515 participants, CAS prevalence reached 57.4%, concentrated within older individuals. Positive correlations were identified with age, systolic blood pressure, a history of hypertension, male gender, and total cholesterol. High-density lipoprotein (HDL) emerged as a protective factor against CAS, with total cholesterol and HDL levels proving significant predictors.
UNASSIGNED: This research illuminates the risk factors linked to CAS and introduces a validated risk scoring tool, highlighted by the logistic classifier\'s consistent performance during training and testing. This tool shows potential for pinpointing high-risk individuals in community health programs, streamlining screening and intervention by clinical physicians. By stressing the significance of managing cholesterol levels, especially HDL, our findings provide actionable insights for CAS prevention. Nonetheless, rigorous validation is paramount to guarantee its practicality and efficacy in real-world scenarios.
UNASSIGNED: A comprehensive approach was employed, integrating traditional epidemiological methods with advanced machine learning techniques, including support vector machines, XGBoost, decision trees, random forests, and logistic regression.
UNASSIGNED: Among 1,515 participants, CAS prevalence reached 57.4%, concentrated within older individuals. Positive correlations were identified with age, systolic blood pressure, a history of hypertension, male gender, and total cholesterol. High-density lipoprotein (HDL) emerged as a protective factor against CAS, with total cholesterol and HDL levels proving significant predictors.
UNASSIGNED: This research illuminates the risk factors linked to CAS and introduces a validated risk scoring tool, highlighted by the logistic classifier\'s consistent performance during training and testing. This tool shows potential for pinpointing high-risk individuals in community health programs, streamlining screening and intervention by clinical physicians. By stressing the significance of managing cholesterol levels, especially HDL, our findings provide actionable insights for CAS prevention. Nonetheless, rigorous validation is paramount to guarantee its practicality and efficacy in real-world scenarios.
摘要：
■本研究旨在调查颈动脉粥样硬化（CAS）的患病率，尤其是老年人，并开发精确的风险评估工具，以促进对高风险个体的筛查和早期干预。
■采用了一种综合方法，将传统的流行病学方法与先进的机器学习技术相结合，包括支持向量机，XGBoost,决策树,随机森林，和逻辑回归。
■在1,515名参与者中，CAS患病率达到57.4%,集中在老年人身上。与年龄呈正相关，收缩压,有高血压史,男性,和总胆固醇。高密度脂蛋白（HDL）成为抗CAS的保护因素，总胆固醇和HDL水平证明了重要的预测因子。
■这项研究阐明了与CAS相关的风险因素，并引入了经过验证的风险评分工具，由逻辑分类器在训练和测试期间的一致性能突出显示。该工具显示了在社区卫生计划中精确定位高危人群的潜力，简化临床医生的筛查和干预。通过强调控制胆固醇水平的重要性，尤其是HDL,我们的研究结果为CAS预防提供了可行的见解。尽管如此，严格的验证对于保证其在现实场景中的实用性和有效性至关重要。
■采用了一种综合方法，将传统的流行病学方法与先进的机器学习技术相结合，包括支持向量机，XGBoost,决策树,随机森林，和逻辑回归。
■在1,515名参与者中，CAS患病率达到57.4%,集中在老年人身上。与年龄呈正相关，收缩压,有高血压史,男性,和总胆固醇。高密度脂蛋白（HDL）成为抗CAS的保护因素，总胆固醇和HDL水平证明了重要的预测因子。
■这项研究阐明了与CAS相关的风险因素，并引入了经过验证的风险评分工具，由逻辑分类器在训练和测试期间的一致性能突出显示。该工具显示了在社区卫生计划中精确定位高危人群的潜力，简化临床医生的筛查和干预。通过强调控制胆固醇水平的重要性，尤其是HDL,我们的研究结果为CAS预防提供了可行的见解。尽管如此，严格的验证对于保证其在现实场景中的实用性和有效性至关重要。
