Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances.It makes an effort to get the better of issues of health service delivery ...Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances.It makes an effort to get the better of issues of health service delivery involving time factor,space and laborious terrains,validating cost-efficiency and finer ingress in both developed and developing countries.Tele health has been categorized into either real-time electronic communication,or store-andforward communication.In recent years,a third-class has been perceived as remote healthcare monitoring or tele health,presuming data obtained via Internet of Things(IOT).Although,tele health data analytics and machine learning have been researched in great depth,there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare sector.Motivated by this fact,in this work a method called,Weighted Bayesian and Polynomial Taylor Deep Network(WB-PTDN)is proposed to improve health prediction in a computationally efficient and accurate manner.First,the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet dataset.Next,with the normalized data as input,Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk analysis.Finally,to obtain reliable predictions concerning tele health data analytics,First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision making.The comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy,true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.展开更多
文摘Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances.It makes an effort to get the better of issues of health service delivery involving time factor,space and laborious terrains,validating cost-efficiency and finer ingress in both developed and developing countries.Tele health has been categorized into either real-time electronic communication,or store-andforward communication.In recent years,a third-class has been perceived as remote healthcare monitoring or tele health,presuming data obtained via Internet of Things(IOT).Although,tele health data analytics and machine learning have been researched in great depth,there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare sector.Motivated by this fact,in this work a method called,Weighted Bayesian and Polynomial Taylor Deep Network(WB-PTDN)is proposed to improve health prediction in a computationally efficient and accurate manner.First,the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet dataset.Next,with the normalized data as input,Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk analysis.Finally,to obtain reliable predictions concerning tele health data analytics,First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision making.The comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy,true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.
