Title: Portable healthcare computing and clinical decision support system enabled by artificial intelligence

Authors: Satish S. Salunkhe; Vinodkumar Jacob; Aditya Tandon; S. Jeevitha; Rakesh Kumar Arora; Shilpa Laddha

Addresses: Computer Engineering Department, Terna Engineering College, India ' Electronics and Communication Engineering, M.A. College of Engineering Kothamangalam, Ernakulam, Kerala, 686666, India ' Department of CS&E, Krishna Engineering College, Ghaziabad, India ' Department of Computer Science and Engineering, Kalasalingam Institute of Technology, Krishnankoil, India ' Department of Computer Science and Engineering, Krishna Engineering College, Ghaziabad, India ' Department of Information Technology, Government College of Engineering, Maharashtra, India

Abstract: In this work, we present a clinical decision support system enabled by the AI methodology including deep neural network (DNN) in association with IoT cloud for the forecasting healthcare of the patient and is analysed by considering chronic kidney disease (CKD) to provide the optimum healthcare services to consumers with its severity level utilising e-health apps. The proposed system gathers patient details from their IoT devices and their healthcare records from the UCI repository are being saved in the cloud aided in the customisation of health therapies for particular populations. In addition, the identification and severity of the disease are performed using a deep neural network (DNN) classifier. A particle swarm optimisation (PSO)-based feature extraction is utilised to increase the effectiveness of the DNN classifier. The standard CKD dataset is used to verify the proposed approach. The proposed approach is evaluated in terms of accuracy, sensitivity, F-score and specificity.

Keywords: artificial intelligence; clinical decision support system; CDSS; deep neural network; DNN; internet of things; IoT; particle swarm optimisation; PSO.

DOI: 10.1504/IJESMS.2022.123955

International Journal of Engineering Systems Modelling and Simulation, 2022 Vol.13 No.3, pp.228 - 233

Received: 24 Jun 2021
Accepted: 16 Aug 2021
Published online: 05 Jul 2022 *

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