ABSTRACT
Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied. The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naive bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.
ABSTRACT
This study was conducted to evaluate the usability of health care apps and its significance in the field of health care. As health care centers are the settings working 24/7 for providing health services to patients. For this study a systematic review, comparison and functionality assessment of selected COVID19 mobile apps was performed. The assessment was performed on two digital platforms, App store on Apple iPhone and Google play store on Android smartphones. Further online search of keywords on Google was also conducted in order to ensure the reliability and validity of apps on these stores. And only those apps were selected which were in English because of the language proficiency of authors. The mobile apps selected were having five features of assessment, a questionnaire, Privacy statement, Precautionary measures if test positive for COVID19, after plans of Pandemic and Region based. The sample was a blend of participants from different fields including students, doctors, patients and software developers. This is quantitative study;a questionnaire was distributed among the selected participants who were familiar with the use of health care mobile apps. The data was analyzed on the basis of gender and age. The results were made on the basis of responses towards effectiveness, efficiency and satisfaction of users towards mobile phone apps. The results show that apps were found 64.5% informative for participants as they helped in fast communication of doctors and patients while sitting at the other corner of world.