Predicting COVID-19 Spread Level using Socio- Economic Indicators and Machine Learning Techniques

Mihoub, Alaeddine; Snoun, Hosni; Krichen, Moez; Salah, Riadh Bel Hadj; Kahia, Montassar

doi:10.1109/smart-tech49988.2020.00041

Cited by 23 publications

(11 citation statements)

References 23 publications

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“…Thousands of diagnostic factors linked to healthcare difficulties can have an impact on the severity of symptoms. As a result, advanced feature selection approaches are trained to determine those attributes that contribute more to the condition of the patient we are concerned with, as possessing unnecessary clinical manifestations considerably raises the possibilities of forecasting inaccuracy [ 6 ]. There are numerous features extraction methodologies.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

Nirmaladevi

Vidhyalakshmi

Edwin

et al. 2022

BioMed Research International

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As an epidemic, COVID-19’s core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

Nirmaladevi

Vidhyalakshmi

Edwin

et al. 2022

BioMed Research International

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“…Em seguida, serão apresentados de forma breve os trabalhos que obtiveram maior relac ¸ão com as questões de pesquisa elencadas nesta RSL. [Mihoub et al 2020] propõem estudar a correlac ¸ão entre a propagac ¸˜do ao COVID-19 e os indicadores socioeconômicos de diferentes países usando técnicas de inteligência artificial. Os níveis de propagac ¸ão foram divididos em três datas diferentes, entre abril e maio de 2020.…”

Section: Trabalhos Relacionadosunclassified

“…O surto do vírus SARS-CoV-2 se espalhou rapidamente por todo o mundo após o primeiro caso relatado no final de dezembro de 2019 e foi declarada uma pandemia global pela Organizac ¸ão Mundial da Saúde -OMS (Organization World Health -WHO) em 11 marc ¸o de 2020 [Mihoub et al 2020]. A COVID-19 afetou a saúde humana como também a economia global.…”

Section: Introduc ¸ãOunclassified

Correlação entre Indicadores Socioeconômicos e a COVID-19: uma revisão sistemática da literatura

Souza

Borchartt²

2022

Anais Estendidos Do XVIII Simpósio Brasileiro De Sistemas De Informação (SBSI Estendido 2022)

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A pandemia da COVID-19, causada pelo novo coronavírus (SARS-CoV-2), iniciou-se em dezembro de 2019 na China, colocando a saúde da população mundial em grande alerta. Este artigo objetiva apresentar uma Revisão Sistemática da Literatura que análise a correlação entre indicadores socioeconômicos e a pandemia da COVID-19 através de técnicas de aprendizagem de máquina, identificando quais técnicas e software de análise de dados foram utilizados. Foram identificados os estudos mais relevantes publicados no período de 2018 a 2022, que abordam o tema proposto. Após a triagem completa a pesquisa destacou 10 artigos selecionados no total de 814 analisados.

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“…Previous studies have demonstrated that the effective size for random forest is varied [14]-[16] and there is no exact number or formula found yet that determines the effective sample size. Referring to [15] that applies random forest to the same database (e.g., World Bank Development Indicators) to classify and predict, we set a limit on number of host parties more than 12, which leaves ve projects for random forest including Biogas, Biomass, Hydro Power, Methane recovery & utilization and Wind Power.…”

Section: Random Forestmentioning

confidence: 99%

Random Forest-based prediction of performance gap of the Clean Development Mechanism projects

Cho

2022

Preprint

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The performance of climate technology implemented under Clean Development Mechanism (CDM) is varied in terms of Certified Emission Reductions (CERs) issuance rate, a ratio of the actual CER issued of the total CER expected, ranged between 2% and 193%. In this study, we propose a novel approach for estimating the CER issuance rate (i.e., lower or higher than 100%) by using random forest algorithms. Unlike previous studies, we associate 1443 indicators related to economy, environment and society from 28 countries with the outcome of CER issuance rate of 3338 CDM projects and examine mixed effects of local context-driven indicators. Data preprocessing and feature selection techniques are applied to extract important indicators with which classification and prediction are conducted across different types of CDM technologies. The highest accuracy and F-measure of the developed prediction model are achieved from wind projects (84%, 92%, respectively). The obtained results exhibit possibilities of capturing the performance gap through machine learning algorithm. Moreover, we recommend adopting local demand-driven indicators to enhance the usability of the proposed approach.

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Predicting COVID-19 Spread Level using Socio- Economic Indicators and Machine Learning Techniques

Cited by 23 publications

References 23 publications

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

Correlação entre Indicadores Socioeconômicos e a COVID-19: uma revisão sistemática da literatura

Random Forest-based prediction of performance gap of the Clean Development Mechanism projects

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