基于K-means和MTLS-SVM算法的生理参数监测系统

摘要

在非医模式的生理参数监测系统中,对监测参数进行学习,可以提高诊断和预测精度.针对多任务时间序列中存在的信息挖掘不充分、预测精度低等问题,将机器学习中的监督和半监督学习方式结合起来对远程健康监护对象进行生理状况预测.该方法用K-means算法将相同类别的数据集群,并使用多任务最小二乘支持向量机(MTLS-SVM)来训练历史数据来进行趋势预测.为了评估该方法的有效性,将MTLS-SVM方法与K-means、MTLS-SVM方法比较,实验结果表明该方法具有较高的预测精度.%In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.