Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage

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.

Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation

“…The IoMT framework addresses the shortage of resources faced by medical teams during the COVID-19 epidemic; not only can the geo-location of COVID-19 patients be tracked by Bluetooth to assess personnel risk [ 26 , 27 , 31 , 40 ], but the technology allows for tracking of patient recovery [ 22 , 33 ]. Chen’s research focuses on visualizing and discussing this important information [ 35 ].…”

Understanding the Pivotal Role of the Vagus Nerve in Health from Pandemics

“…For instance, a decrease in miR146a-5p may indicate HIV infection [ 93 ] and could serve as both a diagnostic and prognostic factor in COVID-19 [ 36 , 37 ]. Consequently, HRV monitoring is undoubtedly worth including in smartphone-based or other expert systems for disease prediction at early stages, or for outcome prediction, which is not yet performed routinely [ 94 , 95 , 96 ].…”

Customer Shopping Behavior Analysis Using RFID and Machine Learning Models

“…The combination of partial least squares-discriminant analysis (PLS-DA) for feature selection and adaptive synthetic sampling (ADASYN) for data balancing demonstrates the best overall performance, offering promising precision medicine prospects for diabetes treatment. Finally, Raihan et al [30] explores the use of smartphone technology to detect the presence of COVID-19, given the challenges and risks posed by the pandemic. To address the class imbalance issue in the COVID-19 dataset, Adaptive Synthetic Sampling (ADASYN) is employed.…”

“…Yet, machine learning algorithms frequently encounter challenges such as outliers and imbalanced datasets, which can reduce accuracy. Research has demonstrated that addressing these issues by applying the Isolation Forest (iForest) method to remove outliers [23][24][25][26][27] and utilizing Adaptive Synthetic Sampling (ADASYN) for balancing imbalanced data [28][29][30][31][32][33][34][35] can lead to improved predictive system performance.…”