Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization

Shi, Beibei; Ye, Hua; Heidari, Ali Asghar; Zheng, Lei; Hu, Zhongyi; Chen, Huiling; Turabieh, Hamza; Mafarja, Majdi; Wu, Peiliang

doi:10.1016/j.jksuci.2021.09.019

Cited by 8 publications

(7 citation statements)

References 109 publications

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“…Furthermore, it can also be used to solve other problems, such as disease diagnoses and financial risk predictions [131,132]. Further, the GLLCSA method can also be used to optimize the hyperparameters of other models and solve more complex optimization problems [133,134].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Zhang¹,

Yan

et al. 2022

Applied Sciences

Self Cite

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“…A similar study was proposed by Hu et al Their model provides an effective strategy for accurate early assessment of Covid-19 and distinguishing disease severity [18]. In another study a proficient intelligence structure for promptly identifying and distinguishing the severity of Covid-19 based on coagulation index was proposed [19]. Alakuş et al helped draw a picture of how the disease could progress with a new artificial intelligence modeling [20].…”

Section: Introductionmentioning

confidence: 90%

Prediction of Mortality in Inpatients of Covid-19 Using Statistical and Artificial Intelligence Approaches: A Case Study in Sakarya

Adak,

Akpinar,

Yildiz

et al. 2024

IEEE Access

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The virus SARS-Cov-2 speared rapidly as pandemic disease and cause serious respiratory distress and death. The parameters such as D-dimer, fibrinogen, C-reactive protein, and serum ferritin become higher indicators for acute inflammation. It is vital to understand whether the disease will progress severely or result in death by using laboratory findings. In this way, a doctor concentrating on the patient can make faster and more accurate decisions about the prognosis of the disease and survey patients using artificial intelligence and statistical methods. Therefore, this study aims to determine the attributes associated with deteriorating prognosis by analyzing the laboratory findings of patients who are followed in Sakarya U. Training and Res. Hospital with Covid-19 by using artificial intelligence and statistical methods. More precisely the general aim of this study is to leverage artificial intelligence and statistical methods to identify specific laboratory markers associated with Covid-19 patient deterioration and mortality, enabling more accurate prognosis and clinical decision-making. Our results demonstrated that high fibrinogen, troponin, albumin, d-dimer, ferritin, intubation, and uric acid level were directly related to mortality in Covid-19 patients. Additionally, the training-based models give higher accuracy results than statistical methods. Thus, these laboratory findings of Covid-19 patients and the training-based models may potentially provide doctors make easier and more accurate decisions about mortality and survey predicting.

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“…Moreover, some methods that use other indicators have also been proposed. Shi et al [ 27 ] created a functional design (EBSO SVM) for the early detection and classification of COVID-19 intensity using coagulation indices. This approach achieved an accuracy of 91.9195%, a Matthew correlation coefficient of 90.529%, sensitivity of 90.9912%, and specificity of 88.5705%.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans

Wen¹,

Liu²,

Liu³

et al. 2023

Computers in Biology and Medicine

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Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization

Cited by 8 publications

References 109 publications

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

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

Prediction of Mortality in Inpatients of Covid-19 Using Statistical and Artificial Intelligence Approaches: A Case Study in Sakarya

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans

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