An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty

Ahmed, Tanvir; Jamil, Mohammad; Hossain, Mohammad Shahadat; Islam, Raihan Ul; Andersson, Karl

doi:10.1007/s12559-021-09978-8

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…The results of Khozeimeh et al's 58 study also showed that one of the most important features related to the mortality of patients with COVID-19 was heart disease. The results of studies of Ahmed et al 54 and Cisterna-García et al 56 similar to the results of our study, identified the features of lung disease, blood pressure, and acute respiratory distress syndrome as effective factors in the development of a predictive survival system.…”

Section: Discussionsupporting

confidence: 92%

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Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Yazdani

Bigdeli

Zahmatkeshan

2023

Health Science Reports

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Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patients

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Section: Discussionsupporting

confidence: 92%

“…data set. 53 Various studies [54][55][56][57][58][59][60][61] were conducted in predicting the survival and mortality of COVID-19 patients using ML techniques. Krajah's et al 59 study showed intubation was the most important features for the survival prediction.…”

Section: Nb Classifier Can Classify All the Cases Of Death Correctly ...mentioning

confidence: 99%

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Yazdani

Bigdeli

Zahmatkeshan

2023

Health Science Reports

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“…None of these features were identified as the ten most important features in predicting survival in our model. In the study of Ahmed et al the characteristics of blood pressure, chronic obstructive pulmonary disease, blood sugar, asthma, chronic kidney disease, obesity, acute respiratory distress syndrome, and pulse oximetry were used as effective factors in developing a predicting survival system [37]. In line with these results, in our study, the important role of blood pressure, kidney disease, and lung problems has been proven in terms of survival prediction.…”

Section: Discussionsupporting

confidence: 75%

Survival Analysis of Patients with COVID-19 using Deep Neural Network and Random Forrest Techniques

Yazdani,

Erfannia,

Farzaneh

et al. 2024

Front Health Inform

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Introduction: The prediction of the survival chance of coronavirus disease 2019 (COVID-19) patients is as important as the early detection of the coronavirus. Since patient mortality, factors may differ by location, this study concentrated on identifying the influential factors and predicting survival for COVID-19 patients using machine learning methods in Fars province, Iran.Material and Methods: The research dataset was extracted in the period January 21, 2020, to September 25, 2020, and contains 25858 hospitalized patients’ records with 51 features. These records were classified into two categories: death (label 1) and survival (label 0). The methodology of this research is CRISP standard. A comparison was made between the efficiency of two deep neural network and random forest algorithms in predicting survival. Modeling steps were done with Python language in the Google Colab environment.Results: Experimental results demonstrated that the deep neural network algorithm had better performance than random forest with accuracy, precision, recall, F-score, and receiver operating characteristic of 97.2%, 100%, 93.54%, 96.66%, and 97.9%, respectively. Based on the results of the random forest model, history of hypertension, chronic neurological disorders, chronic lung diseases, asthma, chronic kidney disease and, heart disease were the most important risk factors related to death.Conclusion: Deployment of our proposed model allows medical professionals to exercise greater caution during the treatment of patients who are most likely to die due to their medical conditions.

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“…Other scholars established a carbon dioxide absorption desorption process optimization system based on the product life cycle theory by using the life cycle cost method [4]. Some scholars applied the principles of supply chain environmental management and resource allocation to build a multi-stage linear solution based on single objective integer programming, solved the problem of mismatch between carbon emission peak and emission reduction benefit indicators, and achieved comprehensive energy conservation and cost reduction effect [5][6]. Therefore, based on integrated intelligence, this paper studies and discusses the optimization of the peak path of industrial carbon emissions in Shanghai.…”

Section: Introductionmentioning

confidence: 99%

Optimization of peak path of industrial carbon emissions in Shanghai based on integrated intelligent algorithm

Lin

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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With the continuous advancement of industrialization, energy consumption is increasing, and carbon emissions are also rising, causing great harm to greenhouse gases and the environment. Based on the objectives of energy conservation and emission reduction, reducing pollutant generation and promoting sustainable economic development, it has become one of the important tasks for our society to increase industrial technology innovation to achieve low energy consumption and high efficiency. Based on Shanghai energy conservation and environmental protection industry as the research object, this paper uses integrated intelligent algorithm to model and analyze. Firstly, the grey system theory is used to build the model of influencing factors and ways of carbon emissions of low-energy industrial enterprises in Shanghai, and then the ant colony optimization method is used to build the distribution map of CO2 emission intensity in the nuclear fusion chemical project in Shanghai society to predict the carbon source value and calculate the carbon dioxide emission reduction. From the experimental results, it can be seen that the optimization efficiency of Shanghai's industrial carbon emission peak path model by integrating intelligent algorithms is in the range of 55% - 72%, the carbon emissions before optimization are 5678kg - 6735kg, and the carbon emissions after optimization are 3673kg - 4231kg. This shows that the optimization model meets the needs of users.

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An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty

Cited by 13 publications

References 32 publications

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

Survival Analysis of Patients with COVID-19 using Deep Neural Network and Random Forrest Techniques

Optimization of peak path of industrial carbon emissions in Shanghai based on integrated intelligent algorithm

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