Portable healthcare computing and clinical decision support system enabled by artificial intelligence. (14th June 2022)

• Record Type: Journal Article
• Authors: Salunkhe, Satish S.; Jacob, Vinodkumar; Tandon, Aditya; Jeevitha, S.; Arora, Rakesh Kumar; Laddha, Shilpa
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 3(2022)
• Page Start: 228
• Page End: 233
• Publication Date: 2022-06-14
• Subjects: artificial intelligence -- clinical decision support system -- CDSS -- deep neural network -- DNN -- internet of things -- IoT -- particle swarm optimisation -- PSO; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

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.