Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia

Wu, Gang; Zhou, Shuai; Wang, Yujin; Li, Xiaoming

doi:10.21203/rs.3.rs-23196/v1

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…This model provided 70.25% sensitivity, 85.98% specificity and 86.78% AUC. For covid-19 detection a LASSO Logistic Regression Model was developed by [42] by using blood test results. The dataset was divided into a ratio of 80:20 and contained 110 samples.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Detection from CBC using Machine Learning Techniques

Akhtar¹,

Akhtar²,

Bakhtawar³

et al. 2021

Int. J. TIM

109

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Covid-19 pandemic has seriously affected the mankind with colossal loss of life around the world. There is a critical requirement for timely and reliable detection of Corona virus patients to give better and early treatment to prevent the spread of the infection. With that being said, current researches have revealed some critical benefits of utilizing complete blood count tests for early detection of COVID-19 positive individuals. In this research we employed different machine learning algorithms using full blood count for the prediction of COVID-19. These algorithms include: “K Nearest Neighbor, Radial Basis Function, Naive Bayes, kStar, PART, Random Forest, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Further, “Accuracy, Recall, Precision, and F-Measure” are the performance evaluation measures that are utilized in this study.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Detection from CBC using Machine Learning Techniques

Akhtar¹,

Akhtar²,

Bakhtawar³

et al. 2021

Int. J. TIM

109

View full text Add to dashboard Cite

show abstract

“…Recent studies have shown the impact of a critical branch of AI methods, especially the integration of ML algorithms for effective prediction models [ 31 ]. However, ML algorithms can learn and discriminate between numerous patterns in the parameters of a routine blood test.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

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Artificial intelligence-driven assessment of radiological images for COVID-19

Bouchareb

Khaniabadi

Kindi

et al. 2021

Computers in Biology and Medicine

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Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

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Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia

Cited by 4 publications

References 5 publications

COVID-19 Detection from CBC using Machine Learning Techniques

COVID-19 Detection from CBC using Machine Learning Techniques

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Artificial intelligence-driven assessment of radiological images for COVID-19

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