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1.
Nursing Open ; 27:27, 2022.
Article in English | MEDLINE | ID: covidwho-2173312

ABSTRACT

AIM: This study aimed to identify the predictors of mortality and ICU requirements in hospitalized COVID-19 Patients with Diabetes.

2.
6th International Conference on Inventive Systems and Control, ICISC 2022 ; 436:775-788, 2022.
Article in English | Scopus | ID: covidwho-2014003

ABSTRACT

This study is divided into risk factor analysis (RFA) and proposed system architecture (PSA). The light gradient boosting machine (LightGBM) algorithm in the RFA will work with the PSA to predict the risk factors. The results, efficacy, and performance will be validated via a ROC-AUC curve. Therefore, a system usability scale (SUS) procedure will be implemented to increase the performance. If the SUS score reaches 85–99 and 100 thresholds, it will be classified as appropriate for use and robust. The prediction score thresholds will be 0–100. If the score is below 25, it will be classified as normal, 26–50 as moderate, 51–70 risk, and 71–100 as severe. Due to a shortage of experienced staff and intelligent technology, it is becoming progressively difficult to reduce COVID-19 fatality rates. In this research, a lightweight mobile application has been suggested from which the significant patterns and factors can be recognised. Furthermore, it will assist both doctors and patients become aware of COVID-19 risk factors and take the required steps to mitigate them. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
24th International Conference on Computer and Information Technology, ICCIT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1714047

ABSTRACT

An automated means for predicting the virus is of utmost importance to help the medical personnel to detect patients, prepare reports and produce results fast and impeccably so that people can get early treatment and prevent future transmissions. In this work, we proposed a COVID19 detection method using chest x-ray images by training and testing pre-trained deep neural network models, such as VGG19, InceptionV3, and Densenet201 individually, and got an accuracy of 96.9%, 95.2%, and 96.7% respectively. Then to bolster the performance of each model, we proposed an average weighted based ensemble approach and got an accuracy of 97.5%, which surpassed the performance of each separate model. © 2021 IEEE.

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