An Enhanced and Scrutinized, Secure Framework for Health Monitoring using IoT
R. Sujatha M.E1, A. Ramanan2, M. Logesh3
1R. Sujatha M.E., P.H.D, Senior Assistant Professor, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, (TamilNadu), India.
2A. Ramanan, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, (TamilNadu), India.
3M. Logesh, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, (TamilNadu), India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 14 April 2019 | Manuscript Published on 24 May 2019 | PP: 211-214 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10420486S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons. In this project have used a nursing EHR system to build predictive models to determine what are all the factors impact death anxiety, a significant problem for the dying patients. Different existing modeling techniques have been used to develop coarse-grained as well as fine-grained models to predict patient outcomes. The coarse-grained models help in predicting the outcome at the end of each hospitalization, whereas fine-grained models help in predicting the outcome at the end of each shift, therefore providing a trajectory of predicted outcomes. Based on different modeling techniques, our results show significantly accurate predictions, due to relatively noise-free data. These models can help in determining effective treatments, lowering healthcare costs, and improving the quality of end-of- life (EOL) care.The DES based Public Key Cryptographic system of Identity Base Encryption is used for encryption of the Digital Signature. To deal with security problems, various schemes based on the Attribute-Based Encryption have been proposed. In this paper, in order to make e-health data’s more secure we use multi party in cloud computing system. Where the health data is encrypted using attributes and key policy. And the user with a particular attribute and key policy alone will be able to decrypt the health data after it is verified by “key distribution centre” and the “secure data distributor”. This technique can be used in medical field for secure storage of patient details and limiting to particular doctor access.
Keywords: Electronic Health Record (EHR), Multi-dimensional Data set, Fine-Grained Models, Multi-Attribute based Encryption, Quantum Cryptography.
Scope of the Article: Community Information Systems