Survival Analysis of COVID-19 Patients With Symptoms Information by Machine Learning Algorithms

Kim, Gwangsu; Yoo, Chang D.; Yang, Seong J.

doi:10.1109/access.2022.3182350

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…Therefore, low levels of oxygen saturations reflect underlying disease severity, the necessity for cardiorespiratory resuscitation, and the potential risk of future decompensation in the absence of adequate medical intervention. ML models have the potential to integrate non-invasive parameters such as age and SpO2 data alongside other clinical parameters to categorise patients according to illness severity, forecast disease progression, and monitor treatment response [46]. Several studies, such as the one conducted by Calvillo-Batllés et al, have developed prediction models to evaluate the severity and mortality risk of COVID-19 patients by analysing a dataset encompassing clinical, laboratory, and imaging data from COVID-19 patients.…”

Section: Ai-assisted Image Analysismentioning

confidence: 99%

Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction

Najjar,

Hossain,

Ahmed

et al. 2024

Preprint

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Background: The COVID-19 pandemic, which has impacted over 222 countries resulting in incalculable losses, has necessitated innovative solutions via machine learning (ML) to tackle the problem of overburdened healthcare systems. This study consolidates research employing ML models for COVID-19 prognosis, evaluates prevalent models and performance, and provides an overview of suitable models and features while offering recommendations for experimental protocols, reproducibility and integration of ML algorithms in clinical settings. Methods: We conducted a review following the PRISMA framework, examining ML utilisation for COVID-19 prediction. Five databases were searched for relevant studies up to 24 January 2023, resulting in 1,824 unique articles. Rigorous selection criteria led to 204 included studies. Top-performing features and models were extracted, with the area under the receiver operating characteristic curve (AUC) evaluation metric used for performance assessment. Results: This systematic review investigated 204 studies on ML models for COVID-19 prognosis across automated diagnosis (18.1%), severity classification (31.9%), and outcome prediction (50%). We identified thirty-four unique features in five categories and twenty-one distinct ML models in six categories. The most prevalent features were chest CT, chest radiographs, and advanced age, while the most frequently employed models were CNN, XGB, and RF. Top-performing models included neural networks (ANN, MLP, DNN), distance-based methods (kNN), ensemble methods (XGB), and regression models (PLS-DA), all exhibiting high AUC values. Conclusion: Machine learning models have shown considerable promise in improving COVID-19 diagnostic accuracy, risk stratification, and outcome prediction. Advancements in ML techniques and their integration with complementary technologies will be essential for expediting decision-making and informing clinical decisions, with long-lasting implications for healthcare systems globally.

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Section: Ai-assisted Image Analysismentioning

confidence: 99%

Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction

Najjar,

Hossain,

Ahmed

et al. 2024

Preprint

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“…Principal component analysis (PCA) was employed to solve the multivariate information overlap problem [50]. It objectively determines the weight of the comprehensive evaluation model by the common factor eigenvalue, which avoids arbitrary subjective weight assignment and is more scientific [51].…”

Section: ) Evaluation Modelmentioning

confidence: 99%

Synchronous Development Strategy of China’s New Four Modernizations: An Analysis Based on Interactive Mode

Zhu

et al. 2023

IEEE Access

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The Chinese government has proposed a synchronous development strategy for new urbanization, industrialization, informatization, and agricultural modernization (called ''New Four Modernization,'' NFM), which provides new ideas for solving social sustainable development problems such as large urbanrural gaps, irrational industrial structures, and environmental pollution. This study aims to examine their interactive modes and provide a theoretical basis and empirical evidence for the synchronous development of NFM. The analysis started with the interaction mechanisms of new urbanization, industrialization, informatization, and agricultural modernization. Principal component analysis (PCA) was used to comprehensively evaluate the level of NFM development. A vector auto-regression (VAR) model was constructed to analyze the interaction effect empirically. The findings were as follows: (1) China's new urbanization, industrialization, informatization, and agricultural modernization development levels were rising, and there was a long-term equilibrium relationship between them. (2) The Granger joint test found that the combined effects of any three developments in new urbanization, industrialization, informatization and agricultural modernization have a significant causal relationship with the development of the other, which showed that the synchronous development of NFM had an inherent motivation. (3) New urbanization occurred at the core of NFM interaction mode and was the main driving force for the development of the other three modernizations. Lastly, this study provides a new case for better understanding the interaction mode of NFM in emerging countries such as China, and provides Chinese experience for developing countries.INDEX TERMS New four modernization, sustainable social development, interactive mode, principal component analysis (PCA), vector auto-regression model (VAR). BRIAN TEO SHENG XIAN received the B.Sc. (Hons.) and Ph.D. degrees from University Putra Malaysia (UPM), Malaysia, in 2008 and 2014, respectively. He is currently an Associate Professor with Management & Science University (MSU), where he is also heading the International Academic Affairs Department. His years of experience in multi-disciplinary research in both business and scientific world have garnered his unique areas of expertise. His research interests include the spectrum of areas, including data mining, data analytics, causal reasoning, the scientific understanding of learning, data visualization, medical tourism, strategic management, complementary and alternative medicine, cosmeceuticals and skin care, and colloidal science.

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“…Moreover, evaluating the symptoms of COVID‐19 because of the identification of the early signs of danger can also help the doctor make historic decisions about the methods of hospitalization, treatment, and discharge of the patient. 34 …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, identifying factors affecting the level of risk and determining the probability of survival is very important. Moreover, evaluating the symptoms of COVID‐19 because of the identification of the early signs of danger can also help the doctor make historic decisions about the methods of hospitalization, treatment, and discharge of the patient 34 …”

Section: Introductionmentioning

confidence: 99%

“…Moreover, evaluating the symptoms of COVID-19 because of the identification of the early signs of danger can also help the doctor make historic decisions about the methods of hospitalization, treatment, and discharge of the patient. 34 Therefore, this study aims to provide a prediction model for the survival of COVID-19 patients in Fasa city. Based on our current knowledge, no model was done on clinical data of this region.…”

mentioning

confidence: 99%

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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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Survival Analysis of COVID-19 Patients With Symptoms Information by Machine Learning Algorithms

Cited by 5 publications

References 34 publications

Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction

Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction

Synchronous Development Strategy of China’s New Four Modernizations: An Analysis Based on Interactive Mode

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

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