Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

Kocadağlı, Ozan; Baygül, Arzu; Gökmen, Neslihan; İncir, Said; Aktan, Çağdaş

doi:10.1016/j.retram.2021.103319

Cited by 15 publications

(17 citation statements)

References 22 publications

(41 reference statements)

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“…The bio clinical markers such as D-dimer, C-reactive protein (CRP), Ferritin, Lactate dehydrogenase (LDH) and Neutrophil-to-Lymphocyte ratio (NLR) levels change drastically in COVID-19 patients . These tests can be further analyzed to predict the severity of a patient (Kocadagli et al 2022). Besides Sars-CoV-2, coughing can also be a symptom of various other infections (Landt et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

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In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

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Section: Introductionmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

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show abstract

“…Muhammad et al ( 2021 ) applied ANN, SVM and other ML models for the prediction of daily COVID-19 cases for Mexico. Kocadagli et al ( 2022 ) used hybrid ML approach for clinical prognosis evaluation of COVID-19 patients at Koc University Hospital Istanbul, Turkey. Xiong et al ( 2022 ) compared SVM, random forest (RF) and logistic regression (LR) models for predicting COVID-19 severity.…”

Section: Introductionmentioning

confidence: 99%

“…Muhammad et al (2021) applied ANN, SVM and other ML models for the prediction of daily COVID-19 cases for Mexico. Kocadagli et al (2022) 2022) performed spatiotemporal COVID-19 incidence forecasting at the county level in the USA using ML approach.…”

Section: Introductionmentioning

confidence: 99%

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Ibrahim¹,

Tulay²,

Abdullahi

2022

Environ Sci Pollut Res

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Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R 2 = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R 2 = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.

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“…Inconsistency, or larger confidence interval, between different datasets concerning the importance of a factor may well reflect the contextual heterogeneity in other variables (Ghahramani et al, 2020; Kermali et al, 2020). The multi-variable statistical/machine learning models (Strobl et al, 2008) are ideal to supplement the single-variable analyses, but in the case of COVID-19 data analysis, are most focused on prediction and diagnosis (An et al, 2020; Li et al, 2020; McCoy et al, 2021; Li et al, 2021; Bennett et al, 2021; Karthikeyan et al, 2021; Aljameel et al, 2021; Mahdavi et al, 2021; Cornelius et al, 2021; Kocadagli et al, 2022; Malik et al, 2022), not on variable evaluation such as in our work. There are more applications of machine learning and artificial intelligence in the context of COVID-19, ranging from drug repurposing to medical assistance (Zeng et al, 2020; Deepthi et al, 2021; Chen et al, 2022; Alafif et al, 2021; Khan et al, 2021; Piccaialli et al, 2021; Dogan et al, 2021; Majeed and Hwang, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort

Ülgen

Çetin

et al. 2022

Preprint

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Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recover as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10~15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contains calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

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Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

Cited by 15 publications

References 22 publications

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort

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