Robust and efficient COVID-19 detection techniques: A machine learning approach

Hasan, Md. Mahadi; Murtaz, Saba Binte; Islam, Muhammad Usama; Sadeq, Muhammad Jafar; Uddin, Jasim

doi:10.1371/journal.pone.0274538

Cited by 5 publications

(4 citation statements)

References 73 publications

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“…Training a machine learning model on imbalanced data can result in biased models that perform sub-optimally compared to balanced data models [ 22 ]. However, the sampling techniques used to address class imbalance may have limitations, such as the risk of overfitting and the possibility of losing valuable information in the minority class [ 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors

Yego,

Nkurunziza,

Kasozi

2023

PLoS ONE

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Universal Health Coverage (UHC) is a global objective aimed at providing equitable access to essential and cost-effective healthcare services, irrespective of individuals’ financial circumstances. Despite efforts to promote UHC through health insurance programs, the uptake in Kenya remains low. This study aimed to explore the factors influencing health insurance uptake and offer insights for effective policy development and outreach programs. The study utilized machine learning techniques on data from the 2021 FinAccess Survey. Among the models examined, the Random Forest model demonstrated the highest performance with notable metrics, including a high Kappa score of 0.9273, Recall score of 0.9640, F1 score of 0.9636, and Accuracy of 0.9636. The study identified several crucial predictors of health insurance uptake, ranked in ascending order of importance by the optimal model, including poverty vulnerability, social security usage, income, education, and marital status. The results suggest that affordability is a significant barrier to health insurance uptake. The study highlights the need to address affordability challenges and implement targeted interventions to improve health insurance uptake in Kenya, thereby advancing progress towards achieving Universal Health Coverage (UHC) and ensuring universal access to quality healthcare services.

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Section: Methodsmentioning

confidence: 99%

Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors

Yego,

Nkurunziza,

Kasozi

2023

PLoS ONE

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“…Ref. [ 32 ] describes a method for detecting SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that aids in the identification of specific ribonucleic acids (RNAs). The method employs an artificial neural network and proposes a model with an estimated accuracy of 98.24%.…”

Section: Background Literaturementioning

confidence: 99%

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

et al. 2022

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Background: Hospitals face a significant problem meeting patients’ medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell–Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

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“…Further, COVID-19 seems to have the possibility to have far-reaching economic and structural consequences for the United Kingdom economy and its workforce. Recently, the indirect costs of lessening and suppressing the pandemic have risen, and with the beginning of COVID-19, existing trends have accelerated [ 6 ], for example, the shift towards online shopping to a great extent and the emergence of more employees who work from home [ 7 , 8 ]. Moreover, because of national-lockdowns and travel restrictions imposed by COVID-19; the UK’s domestic and international commerce has been negatively affected, which has further impacted numerous firms and organisations.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Brexit and COVID-19’s influence on the UK economy: A data analysis

et al. 2023

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The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

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Robust and efficient COVID-19 detection techniques: A machine learning approach

Cited by 5 publications

References 73 publications

Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors

Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Evaluating the Brexit and COVID-19’s influence on the UK economy: A data analysis

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