目的:探讨郑州市中老年人群心血管病(CVD)的发生风险。方法:2014年8月至2015年12月,从河南省郑州市6个社区筛选年龄40~74岁的常驻居民1993人。采用Framingham10年冠心病风险评分标准评估CVD的发生风险,采用logistic回归分析CVD的危险因...目的:探讨郑州市中老年人群心血管病(CVD)的发生风险。方法:2014年8月至2015年12月,从河南省郑州市6个社区筛选年龄40~74岁的常驻居民1993人。采用Framingham10年冠心病风险评分标准评估CVD的发生风险,采用logistic回归分析CVD的危险因素。结果:研究人群未来10 a CVD高发生风险的比例为40.6%,且男性高于女性(47.1%vs 37.6%,P<0.001)。Logistic回归分析显示,男性(B=0.524,OR=1.690,95%CI为1.377~2.073)、腹型肥胖(B=0.709,OR=2.031,95%CI为1.632~2.529)、TG(B=0.097,OR=1.102,95%CI为1.023~1.187)、尿白蛋白异常(B=0.670,OR=1.955,95%CI为1.486~2.572)均是CVD高发生风险的危险因素。结论:郑州市中老年人群未来10 a CVD的发生风险很高;加强对中老年人群尤其是男性,合并腹型肥胖、高血脂、高血压、糖尿病以及糖尿病高危人群的CVD一级预防和管理,可能有效降低CVD的发生风险。展开更多
The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA),this letter proposes the Multi-resolution LS-SVM (MLS-SVM).The proposed alg...The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA),this letter proposes the Multi-resolution LS-SVM (MLS-SVM).The proposed algorithm has the same theoretical framework as MRA but with better approximation ability.At a fixed scale MLS-SVM is a classical LS-SVM,but MLS-SVM can gradually approximate the target function at different scales.In experiments,the MLS-SVM is used for nonlinear system identification,and achieves better identification accuracy.展开更多
文摘目的:探讨郑州市中老年人群心血管病(CVD)的发生风险。方法:2014年8月至2015年12月,从河南省郑州市6个社区筛选年龄40~74岁的常驻居民1993人。采用Framingham10年冠心病风险评分标准评估CVD的发生风险,采用logistic回归分析CVD的危险因素。结果:研究人群未来10 a CVD高发生风险的比例为40.6%,且男性高于女性(47.1%vs 37.6%,P<0.001)。Logistic回归分析显示,男性(B=0.524,OR=1.690,95%CI为1.377~2.073)、腹型肥胖(B=0.709,OR=2.031,95%CI为1.632~2.529)、TG(B=0.097,OR=1.102,95%CI为1.023~1.187)、尿白蛋白异常(B=0.670,OR=1.955,95%CI为1.486~2.572)均是CVD高发生风险的危险因素。结论:郑州市中老年人群未来10 a CVD的发生风险很高;加强对中老年人群尤其是男性,合并腹型肥胖、高血脂、高血压、糖尿病以及糖尿病高危人群的CVD一级预防和管理,可能有效降低CVD的发生风险。
文摘The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA),this letter proposes the Multi-resolution LS-SVM (MLS-SVM).The proposed algorithm has the same theoretical framework as MRA but with better approximation ability.At a fixed scale MLS-SVM is a classical LS-SVM,but MLS-SVM can gradually approximate the target function at different scales.In experiments,the MLS-SVM is used for nonlinear system identification,and achieves better identification accuracy.
