Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

Abirami, R. Sudha; Kumar, Girish

doi:10.1007/s42979-021-00965-2

Cited by 11 publications

(3 citation statements)

References 25 publications

(23 reference statements)

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“…Support vector machine, naïve bayes, linear regression, decision tree, etc. are some efficient classification algorithms [ 30 ]. The machine may also learn by extracting features from unlabeled data in what is known as unsupervised ML.…”

Section: Ai Modelsmentioning

confidence: 99%

A survey of machine learning-based methods for COVID-19 medical image analysis

Sailunaz

Özyer

Rokne

et al. 2023

Med Biol Eng Comput

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The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.

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Section: Ai Modelsmentioning

confidence: 99%

A survey of machine learning-based methods for COVID-19 medical image analysis

Sailunaz

Özyer

Rokne

et al. 2023

Med Biol Eng Comput

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“…Data were standardized using conditional encoding for each attribute. The Covid-19 severity recognition model has 15 features, and it is a four-modal combination of clinical signs, physiological symptoms, demographic information, epidemiological information, laboratory test, and CT imaging (including the percentage of lung severity) (Abirami & Kumar, 2022). In this model, 37% of the samples are asymptomatic, 33.32% are mild, 5% are moderate, 4.04% are severe, and 20.64% are critical.…”

Section: Covid-19 Data Collection and Understandingmentioning

confidence: 99%

A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality Covid-19 Severity Recognition

Saraei,

Rahmani,

Rajebi

et al. 2023

IJIE

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In March 2020, the world health organization introduced a new infectious pandemic called “novel coronavirus disease” or “Covid-19”, origin dates back to World War II (1939) and spread from the city of Wuhan in China (2019). The severity of the outbreak affected the health of abundant folk worldwide. This bred the emergence of unimodal artificial intelligence approaches in the diagnosis of coronavirus disease but solely led to a significant percentage of false-negative results. In this paper, we combined 2500 Covid-19 multimodal data based on Early Fusion Type-I (EFT1) architecture as a severity recognition model for the classification task. We designed and implemented one-step systems of automatic comparative machine learning (AutoCML) and automatic comparative machine learning based on important feature selection (AutoIFSCML). We utilized our posed assessment method called “Descended Composite Scores Average (DCSA)”. In AutoCML, Extreme Gradient Boost (DCSA=0.998) and in AutoIFSCML, Random Forest (DCSA=0.960) demonstrated the best performance for multimodality Covid-19 severity recognition while 70% of the characteristics with high DCSA were chosen by the internal important features selection system (AutoIFS) to enter the AutoCML system. The DCSA-based designed systems can be useful in implementing fine-tuned machine learning models in medical processes by leveraging the capacities and performances of the model in all methods. As well as, ensemble learning made sounds good among evaluated traditional models in systems.

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“…Previous reviews of the use of ML or AI tools in the context of COVID-19 are either very broad or deal with narrow issues. Broad reviews [14][15][16] tackle a wide variety of techniques and issues that are sometimes unrelated to clinical decision issues in COVID-19 patients, such as the epidemiological model of disease transmission [16]. Others deal with the problem of disease diagnosis in general [17].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

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Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

Cited by 11 publications

References 25 publications

A survey of machine learning-based methods for COVID-19 medical image analysis

A survey of machine learning-based methods for COVID-19 medical image analysis

A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality Covid-19 Severity Recognition

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

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