摘要: In this paper, we propose to employ a statistical prediction model assess medical data accuracy. To end, the purpose of paper is perform using Auto Regressive (AR) model. The experimental results which prove efficiency proposed approach are reported based on three performance criteria namely Root Mean Square Error, Absolute Error and Theil Inequality Coefficient.
sci-hub.se 参考文章(7)
Ahmed Mehaoua, Borko Furht, Ionut Cardei, Osman Salem, Anthony Marcus, A Mobile Device Prototype Application for the Detection and Prediction of Node Faults in Wireless Sensor Networks arXiv: Networking and Internet Architecture. ,(2014)
Yang Yang, Qian Liu, Zhipeng Gao, Xuesong Qiu, Luoming Meng, Data Fault Detection in Medical Sensor Networks Sensors. ,vol. 15, pp. 6066- 6090 ,(2015) , 10.3390/S150306066
Shah Ahsanul Haque, Mustafizur Rahman, Syed Mahfuzul Aziz, Sensor anomaly detection in wireless sensor networks for healthcare. Sensors. ,vol. 15, pp. 8764- 8786 ,(2015) , 10.3390/S150408764
Keith Knight, Consistency of Akaike's Information Criterion for Infinite Variance Autoregressive Processes Annals of Statistics. ,vol. 17, pp. 824- 840 ,(1989) , 10.1214/AOS/1176347145
Atif Manzoor, Hong-Linh Truong, Schahram Dustdar, Quality of Context: models and applications for context-aware systems in pervasive environments Knowledge Engineering Review. ,vol. 29, pp. 154- 170 ,(2014) , 10.1017/S0269888914000034
Walter Zucchini, An introduction to model selection Journal of Mathematical Psychology. ,vol. 44, pp. 41- 61 ,(2000) , 10.1006/JMPS.1999.1276
Girik Pachauri, Sandeep Sharma, Anomaly Detection in Medical Wireless Sensor Networks using Machine Learning Algorithms Procedia Computer Science. ,vol. 70, pp. 325- 333 ,(2015) , 10.1016/J.PROCS.2015.10.026