Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm.

Autor:	Mohanty MD; Department of Electrical Engineering, Campus 1, Technische Universität, 21073 Hamburg, Germany., Das A; Department of Electronics and Communication Engineering, Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar 701030, India., Mohanty MN; Department of Electronics and Communication Engineering, Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar 701030, India., Altameem A; Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh 11533, Saudi Arabia., Nayak SR; Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida 201303, India., Saudagar AKJ; Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia., Poonia RC; Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, India.
Jazyk:	angličtina
Zdroj:	Healthcare (Basel, Switzerland) [Healthcare (Basel)] 2022 Jul 09; Vol. 10 (7). Date of Electronic Publication: 2022 Jul 09.
DOI:	10.3390/healthcare10071275
Abstrakt:	Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. Methods: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. Results: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu