Title: Integrated e-healthcare management system using machine learning and flask

Authors: Sanjeev Kumar; Jaya Ojha; Mayank Mani Tripathi; Kirtika Garg

Addresses: Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, India ' Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, India ' Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, India ' Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, India

Abstract: Healthcare is one of the flourishing sectors in each developed and emerging economy. Due to this vast COVID-19 pandemic, the traditional healthcare system cannot provide adequate facilities due to a lack of interactions between doctors and patients. In such conditions, e-healthcare is contributing towards the accelerating growth within the healthcare industry by providing the latest information technology to support information search and communication processes. Besides this, a machine learning algorithm is used to intensify the smartness of the healthcare industry. The five major components of an e-healthcare system are cost-saving, virtual networking, electronic medical record physician-patient relationships and privacy concerns. Our proposed system provides location-based e-prescribing, e-reports, disease prediction, and suggesting treatments and emergency services with a single click, so it is better than another existing system.

Keywords: e-healthcare; location-based; disease prediction; treatments; e-reports; e-prescription.

DOI: 10.1504/IJEH.2023.128607

International Journal of Electronic Healthcare, 2023 Vol.13 No.1, pp.71 - 90

Received: 29 Jun 2021
Accepted: 28 Oct 2022
Published online: 27 Jan 2023 *