Development of a smartphone-based expert system for COVID-19 risk prediction at early stage

Article

Raihan, M., Hassan, Md Mehedi, Hasan, Towhid, Bulbul, Abdullah Al-Mamun, Hasan, Md Kamrul, Hossain, Md Shahadat, Roy, Dipa Shuvo and Awal, Md Abdul. 2022. "Development of a smartphone-based expert system for COVID-19 risk prediction at early stage." Bioengineering. 9 (7). https://doi.org/10.3390/bioengineering9070281
Article Title

Development of a smartphone-based expert system for COVID-19 risk prediction at early stage

ERA Journal ID211874
Article CategoryArticle
AuthorsRaihan, M., Hassan, Md Mehedi, Hasan, Towhid, Bulbul, Abdullah Al-Mamun, Hasan, Md Kamrul, Hossain, Md Shahadat, Roy, Dipa Shuvo and Awal, Md Abdul
Journal TitleBioengineering
Journal Citation9 (7)
Number of Pages18
Year2022
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2306-5354
Digital Object Identifier (DOI)https://doi.org/10.3390/bioengineering9070281
Web Address (URL)https://www.mdpi.com/2306-5354/9/7/281
Abstract

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 2.82% positive and 97.18% 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 97.91%, 97.81%, and 73.37%, 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.

Keywordsadaptive synthetic sampling; Android or web-based user applications; COVID-19 prediction; feature selection methods; machine learning classifiers
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Byline AffiliationsNorth Western University, Bangladesh
Khulna University, Bangladesh
Khulna University of Engineering and Technology, Bangladesh
International University of Business Agriculture and Technology, Bangladesh
Visvesvaraya Technological University, India
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