Open Access

ARTICLE

Ensemble Deep Learning for IoT Based COVID 19 Health Care Pollution Monitor

Nithya Rekha Sivakumar^*

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Nithya Rekha Sivakumar. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2383-2398. https://doi.org/10.32604/iasc.2023.028574

Received 13 February 2022; Accepted 29 April 2022; Issue published 19 July 2022

Abstract

Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data or feature in a Cloud server to minimize concentrations of air pollutants. Once integrated with patient management systems, this solution would minimize pollution emitted via patient’s sensors by offering spaces in the cloud server when pollution thresholds are reached. Second, the Gini Index factor information gain technique eliminates unimportant and redundant attributes while selecting the most relevant, reducing computing overhead and optimizing system performance. Finally, the COVID-19 disease prognosis ensemble deep learning-based classifier is constructed. Experimental analysis is planned to measure the prediction accuracy, error, precision and recall for different numbers of patients. Experimental results show that prediction accuracy is improved by 8%, error rate was reduced by 47% and prediction time is minimized by 36% compared to existing methods.

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Keywords

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