Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier

Doewes, Rumi Iqbal; Nair, Rajit; Sharma, Tripti

doi:10.1108/wje-03-2021-0174

Cited by 21 publications

(19 citation statements)

References 44 publications

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“…For fulfilling the purpose of this paper, both pneumonia classes are merged into one. Images from the Kaggle chest X-ray images (pneumonia) dataset and the COVID-19 image data collection were merged into a combined dataset [ 26 ]. The total number of images in the combined dataset is 6,310, where 454 were COVID-19 cases, 4,273 were pneumonia cases, and 1,583 were normal cases.…”

Section: Methodsmentioning

confidence: 99%

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network

Ragab

Alshehri

Alhakamy

et al. 2022

Computational Intelligence and Neuroscience

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It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model’s training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.

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Section: Methodsmentioning

confidence: 99%

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network

Ragab

Alshehri

Alhakamy

et al. 2022

Computational Intelligence and Neuroscience

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“…Synthetic Minority Over-sampling Technique allows generating additional data samples through variations of each blood feature 28 , 31 , 33 . A game theory-based Shapley value method can provide a reliable, feature extraction 83 , 84 An ensemble learning based on combining several classification algorithms and generating a final prediction, for instance, by the voting procedure allows improving a predictive model performance 38 , 54 , 85 , 86 , 87 , 88 .…”

Section: Discussionmentioning

confidence: 99%

Predictive models for COVID-19 detection using routine blood tests and machine learning

Kistenev

Vrazhnov

Шнайдер

et al. 2022

Heliyon

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“…In recent years, machine learning (ML) has been utilized as a method to forecast and predict COVID-19 cases [1][2][3]. It has been used in Polymerase Chain Reaction (PCR) tests to predict positive results for COVID-19 infections.…”

Section: Introductionmentioning

confidence: 99%

“…It has been used in Polymerase Chain Reaction (PCR) tests to predict positive results for COVID-19 infections. Therefore, ML can reduce the strain on health care systems that are already overwhelmed by COVID-19 [1,4]. Ismail et al [5] proposed a new model for positive COVID-19 prediction using binary features, including the patient's profile, such as age and five clinical symptoms.…”

Section: Introductionmentioning

confidence: 99%

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

Ismail¹,

Alsalamah²,

Mohamed³

2023

Computers, Materials &Amp; Continua

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As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent predictive method using stacked-based ensemble learning (GA-Stacking) optimized by a Genetic Algorithm (GA) for outbreak prediction and health decision aided processes. GA-Stacking utilizes five well-known classifiers, including Decision Tree (DT), Random Forest (RF), RIGID regression, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost), at its first level. It also introduces GA to identify comparisons to forecast the number, combination, and trust of these base classifiers based on the Mean Squared Error (MSE) as a fitness function. At the second level of the stacked ensemble model, a Linear Regression (LR) classifier is used to produce the final prediction. The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering, Johns Hopkins University, which consisted of 10,722 data samples. The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99% for the three selected countries. Furthermore, the proposed model achieved good performance when compared with existing baggingbased approaches. The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC, 2023, vol.74, no.2 to predict the epidemic trend for other countries when comparing preventive and control measures.

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Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier

Cited by 21 publications

References 44 publications

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network

Predictive models for COVID-19 detection using routine blood tests and machine learning

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

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