Internet of things sensor assisted security and quality analysis for health care data sets using artificial intelligent based heuristic health management system

Abstract The developments in the medical systems, especially in health care management systems, play a vital role in patients. The effective management of health records leads to an increase in the importance of the healthcare management system all over the world. A real-time health monitoring system is a key zone for the Internet of Things (IoT) sensor technology in human services using Big Data Analytics. The major challenge that has to do with the health care data sets is security and privacy. In this paper, an artificial intelligence-based heuristic health management system has been designed and developed. This system is exceptionally close to improve the security and privacy of the live datasets of patients and the association of medicinal services over its different viewpoints. These services include the capacity for specialists, experts, attendants, and staff to settle on better decisions faster. Moreover, security and quality of data by configuration should be a part of any IoT use case, task or arrangement. Utilizing IoT assisted artificial intelligent based heuristic health management system intends to improve and minimize the security risk on health care data sets with assisted IoT sensors. The experimental results show promising outcomes in terms of various performance factors. The system attains precision as 99.75%, error rate as 0.0646 and predicted positive condition rate as 98.46%, Informedness as 98.6% and accuracy as 99.66%. The system is implemented using the MATLAB program.

[1]  Hüseyin Çakir,et al.  Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review , 2015, ArXiv.

[2]  Aamir Saeed Malik,et al.  Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study , 2019, Measurement.

[3]  Masoud Riazi,et al.  A comparison of methods for denoising of well test pressure data , 2018, Journal of Petroleum Exploration and Production Technology.

[4]  Ignacio Rojas,et al.  Design, implementation and validation of a novel open framework for agile development of mobile health applications , 2015, BioMedical Engineering OnLine.

[5]  Michelle R. Hribar,et al.  Secondary Use of EHR Timestamp data: Validation and Application for Workflow Optimization , 2015, AMIA.

[6]  Luis Montiel,et al.  A heuristic approach for the stochastic optimization of mine production schedules , 2017, J. Heuristics.

[7]  Sandeep Kaushik,et al.  Big data in healthcare: management, analysis and future prospects , 2019, Journal of Big Data.

[8]  Brian G. Arndt,et al.  Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations , 2017, The Annals of Family Medicine.

[9]  A. Kamimura,et al.  Patient centeredness: The perspectives of uninsured primary care patients in the United States , 2019, International Journal of Care Coordination.

[10]  Jung-Ryul Lee,et al.  Aircraft integrated structural health monitoring using lasers, piezoelectricity, and fiber optics , 2018, Measurement.

[11]  Isabel de la Torre Díez,et al.  Advances and Current State of the Security and Privacy in Electronic Health Records: Survey from a Social Perspective , 2012, Journal of Medical Systems.

[12]  D. Heisey-Grove,et al.  Physician Opinions about EHR Use by EHR Experience and by Whether the Practice had optimized its EHR Use , 2016, Journal of health & medical informatics.

[13]  Olga Gerget,et al.  Medical Data Processing System based on Neural Network and Genetic Algorithm , 2014 .

[14]  Clemens Scott Kruse,et al.  Security Techniques for the Electronic Health Records , 2017, Journal of Medical Systems.

[15]  P. Mohamed Shakeel,et al.  Developing brain abnormality recognize system using multi-objective pattern producing neural network , 2018, Journal of Ambient Intelligence and Humanized Computing.

[16]  Yu Xue,et al.  An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design , 2017 .