Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

Barbosa, Valter Augusto de Freitas; Gomes, Juliana Carneiro; Santana, Maíra Araújo de; Albuquerque, Jeniffer Emídio de Almeida; Souza, Ricardo Emmanuel de; Santos, Wellington Pinheiro dos

doi:10.1007/s42600-020-00112-5

Cited by 45 publications

(42 citation statements)

References 59 publications

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“…Table 5 lists the comparison between DF-COVID-19 and the other studies which used the Albert Einstein dataset 26 . DF-COVID-19 dramatically outperformed ER-CoV 61 , ANN with SMOTE 62 , Bayes Net 63 , and SVM 64 in terms of accuracy, AUC, and specificity. However, DF-COVID-19 and Bayes Net 63 achieved very similar sensitivity.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

Deep forest model for diagnosing COVID-19 from routine blood tests

AlJame

Imtiaz

Ahmad

et al. 2021

Sci Rep

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The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

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Section: Performance Evaluation and Discussionmentioning

confidence: 99%

Deep forest model for diagnosing COVID-19 from routine blood tests

AlJame

Imtiaz

Ahmad

et al. 2021

Sci Rep

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“…Barbosa et al (20) considered the fact that, in many regions of the world, RNA testing is not always available due to the scarcity of inputs, created HegIA, an intelligent system based on Bayes Networks and Random Forests to aid at the diagnosis of Covid-19 based on blood tests from 24 blood tests. The performance is close to RT-PCR (Reverse Transcription Polymerase Chain Reaction) for symptomatic individuals, though coronavirus RNA is not searched (20). HegIA is a fully functional system, available for free use, to provide low-cost rapid testing.…”

Section: Related Workmentioning

confidence: 99%

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Silva

Lima

Silva

et al. 2021

Front. Public Health

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Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus.Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics.Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%.Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

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“…In this section the DF-COVID-19 model is evaluated by comparing it with other state-of-the-art approaches 13,27,[60][61][62][63][64] . For comparison fairness, both DF-COVID-19 and previous studies used the same dataset.…”

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

“…One possible reason is the deep forest model on which DF-COVID-19 is based. The last experiment in this section demonstrates the effectiveness of DF-COVID-19 against various studies [61][62][63][64] .…”

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

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Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

AlJame

Imtiaz

Ahmad

et al. 2021

Preprint

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The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and poses considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

show abstract

Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

Cited by 45 publications

References 59 publications

Deep forest model for diagnosing COVID-19 from routine blood tests

Deep forest model for diagnosing COVID-19 from routine blood tests

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

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