Cargando…
Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage
COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to differe...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9311761/ https://www.ncbi.nlm.nih.gov/pubmed/35877332 http://dx.doi.org/10.3390/bioengineering9070281 |
_version_ | 1784753672628994048 |
---|---|
author | Raihan, M. Hassan, Md. Mehedi Hasan, Towhid Bulbul, Abdullah Al-Mamun Hasan, Md. Kamrul Hossain, Md. Shahadat Roy, Dipa Shuvo Awal, Md. Abdul |
author_facet | Raihan, M. Hassan, Md. Mehedi Hasan, Towhid Bulbul, Abdullah Al-Mamun Hasan, Md. Kamrul Hossain, Md. Shahadat Roy, Dipa Shuvo Awal, Md. Abdul |
author_sort | Raihan, M. |
collection | PubMed |
description | COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has [Formula: see text] positive and [Formula: see text] negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved [Formula: see text] , [Formula: see text] , and [Formula: see text] , respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users’ questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository. |
format | Online Article Text |
id | pubmed-9311761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93117612022-07-26 Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage Raihan, M. Hassan, Md. Mehedi Hasan, Towhid Bulbul, Abdullah Al-Mamun Hasan, Md. Kamrul Hossain, Md. Shahadat Roy, Dipa Shuvo Awal, Md. Abdul Bioengineering (Basel) Article COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has [Formula: see text] positive and [Formula: see text] negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved [Formula: see text] , [Formula: see text] , and [Formula: see text] , respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users’ questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository. MDPI 2022-06-27 /pmc/articles/PMC9311761/ /pubmed/35877332 http://dx.doi.org/10.3390/bioengineering9070281 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Raihan, M. Hassan, Md. Mehedi Hasan, Towhid Bulbul, Abdullah Al-Mamun Hasan, Md. Kamrul Hossain, Md. Shahadat Roy, Dipa Shuvo Awal, Md. Abdul Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage |
title | Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage |
title_full | Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage |
title_fullStr | Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage |
title_full_unstemmed | Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage |
title_short | Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage |
title_sort | development of a smartphone-based expert system for covid-19 risk prediction at early stage |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9311761/ https://www.ncbi.nlm.nih.gov/pubmed/35877332 http://dx.doi.org/10.3390/bioengineering9070281 |
work_keys_str_mv | AT raihanm developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT hassanmdmehedi developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT hasantowhid developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT bulbulabdullahalmamun developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT hasanmdkamrul developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT hossainmdshahadat developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT roydipashuvo developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage AT awalmdabdul developmentofasmartphonebasedexpertsystemforcovid19riskpredictionatearlystage |