A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images

Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Garcia‐Zapirain, Begonya; Mostafa, Salama A.; Maashi, Mashael; Al-Waisy, Alaa S.; Subhi, Mohammed Ahmed; Mutlag, Ammar Awad; Le, Dac‐Nhuong

doi:10.32604/cmc.2021.012874

Cited by 70 publications

(43 citation statements)

References 43 publications

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“…Finally, we use Naïve Bayes (NB) classifier to achieve the highest prediction accuracy with the fewest input features. Using TOPSIS MCDM, we successfully screened out "independent risk factors" that predict the severity of COVID-19 [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

et al. 2021

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The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.

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Section: Introductionmentioning

confidence: 99%

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

et al. 2021

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“…All these 27 datasets were used to train and validate the 29 types of chest X-ray classification models. A comprehensive study was performed to understand the performance of automatic detection of COVID-19 based on medical images [ 22 ]. This study uses COVID-19 and normal X-ray images and adopts transfer learning to increase the accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…This model helps to diagnose the COVID-19 virus in chest X-ray images and is composed of four primary stages: image pre-processing, image classification, features extraction and fusion. Mohammed et al [ 22 ] have proposed an automatic prediction to identify COVID-19 for discriminating automatically between normal and COVID-19 infected people in X-ray images. To accomplish this, they used traditional ML methods such as SVM, NN, DT and kNN techniques.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review on AI/ML approaches against COVID-19 outbreak

Doğan

Tiwari

Jabbar³

et al. 2021

Complex Intell. Syst.

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A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

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“…This classifier is often explored in medical applications because it allows a good visualisation of the decision [22]; but, according to studies in [39,62,63], the Neural Network and Deep Leaning methods are effective and have the best accuracy for many applications. The Deep Leaningbased method has been used for COVID-19 detection based on X-ray image analysis [64], natural language processing [65], and epidemiology forecasting [66]. The Deep Learning and Neural Network methods can be effective for a reliability analysis.…”

Section: Discussionmentioning

confidence: 99%

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

2021

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Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.

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A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images

Cited by 70 publications

References 43 publications

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

A systematic review on AI/ML approaches against COVID-19 outbreak

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

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