To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kerne...To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.展开更多
近红外光谱的相对测量对实现人体血糖浓度的在体高精度检测具有重要意义。离体检测中常用的相似背景扣除以及双光路设计等方法不适合人体的复杂背景变化,而基于位置的参考测量方法被认为是有希望实现在体参考测量的方法之一。因此课题...近红外光谱的相对测量对实现人体血糖浓度的在体高精度检测具有重要意义。离体检测中常用的相似背景扣除以及双光路设计等方法不适合人体的复杂背景变化,而基于位置的参考测量方法被认为是有希望实现在体参考测量的方法之一。因此课题组提出差动式浮动基准参考测量方法来实现在体的相对测量。差动式浮动基准参考测量方法是一种具有普适性的参考测量方法,在实际应用中面临着径向检测距离的确定和差动检测信号中有效信号提取的问题。在差动式浮动基准参考测量方法的基础上,提出了基于NAS(net analyte signal)-VIP(variable importance in projection)-SPXY(sample set partitioning based on joint X-Y distances)-PLS(partial least square)的差动浮动基准测量方法,在离体实验中验证了该方法的可行性。结果表明经过该方法处理后,模型的均方根误差明显降低,相关系数也有了一定的提高。对该方法在人体实验中的有效性进行了研究,结果也表明该方法处理后所建模型的精密度和准确性有了明显改善。展开更多
文摘To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.
文摘近红外光谱的相对测量对实现人体血糖浓度的在体高精度检测具有重要意义。离体检测中常用的相似背景扣除以及双光路设计等方法不适合人体的复杂背景变化,而基于位置的参考测量方法被认为是有希望实现在体参考测量的方法之一。因此课题组提出差动式浮动基准参考测量方法来实现在体的相对测量。差动式浮动基准参考测量方法是一种具有普适性的参考测量方法,在实际应用中面临着径向检测距离的确定和差动检测信号中有效信号提取的问题。在差动式浮动基准参考测量方法的基础上,提出了基于NAS(net analyte signal)-VIP(variable importance in projection)-SPXY(sample set partitioning based on joint X-Y distances)-PLS(partial least square)的差动浮动基准测量方法,在离体实验中验证了该方法的可行性。结果表明经过该方法处理后,模型的均方根误差明显降低,相关系数也有了一定的提高。对该方法在人体实验中的有效性进行了研究,结果也表明该方法处理后所建模型的精密度和准确性有了明显改善。
