Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Hu, Jiao; Han, Zhengyuan; Heidari, Ali Asghar; Shou, Yeqi; Ye, Hua; Wang, Liangxing; Huang, Xiaoying; Chen, Huiling; Chen, Yanfan; Wu, Peiliang

doi:10.1016/j.compbiomed.2021.105166

Cited by 72 publications

(29 citation statements)

References 115 publications

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“…Hu et al [ 48 ] proposed a framework based on enhanced binary Harris hawk optimization (HHO) in conjunction with an extreme kernel learning machine (KELM). They used specular reflection learning to improve the original HHO algorithm.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…They used a big gene expression cancer (RNA-Seq) dataset with 20531 features to test their model. Hu et al [294] employed the HHO with the Extreme Learning Machine (ELM) to detect COVID-19 severity using blood gas analysis. They used Specular Reflection Learning and named the new HHO algorithm HHOSRL.…”

Section: Coronavirus Covid-19mentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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“…Specifically, average result (Avg) and standard deviation (Std) represent the average prediction result and standard deviation of each model under ten experiments, respectively. To find the best model, we evaluated each model using 10-fold cross-validation, which was adopted in many studies [43,44,113]. Moreover, the prediction results were analyzed by using the common metrics [114][115][116], including accuracy (ACC), sensitivity (Sens), specificity (Spec), and the Matthews correlation coefficient (MCC).…”

Section: Condition Configurationmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

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College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

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Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Cited by 72 publications

References 115 publications

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

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

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

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