Clinical Signs and Symptoms Associated with COVID-19: A Cross Sectional Study

Romero-Castro, Norma Samanta; Colín-Hernández, Israel; Godoy-Reyes, María Estela; Hernández-Hernández, Margarita; García-Verónica, Alicia; Paredes-Solís, Sergio; Reyes-Fernández, Salvador

doi:10.4067/s0718-381x2022000100112

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…It is important to know that the dataset consists of medical records from COVID-19 patients. Besides, sneezing and coughing are known as COVID-19 symptom [39,40]. Therefore, sneezing and coughing are important for dengue prediction but not necessarily are dengue symptoms.…”

Section: Discussionmentioning

confidence: 99%

Determining important features for dengue diagnosis using feature selection methods

Bria,

Nani,

Siki

et al. 2024

Preprint

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Objectives: This research aims to determine the important features including symptoms and risk factors for dengue diagnosis. Methods: The dataset for this study is in the form of medical records collected from two hospitals in East Nusa Tenggara Province including Kewapante and Soe hospitals. Feature selection methods including feature importance, recursive feature elimination, correlation matrix from Pearson correlation coefficient and KBest were leveraged to determine important features. Important features were also gathered from fifteen Indonesian medical doctors to confirm the results. To obtain the best significant features for dengue prediction, we used six machine learning techniques including logistic regression, k-nearest neighbors, eXtreme gradient boosting, random forests, Naive Bayes and support vector machines. Results. The random forest classifier yields the highest accuracy for the best combination of features with the accuracy of 0.93 (LR: 0.90 (0.04), KNN: 0.89 (0.04), XGBoost: 0.91 (0.03), RF: 0.93 (0.04), NB: 0.88 (0.09), SVM: 0.89 (0.04)) and precision of 0.90 (LR: 0.86 (0.22), KNN: 0.67 (0.14), XGBoost: 0.77 (0.13), RF: 0.90 (0.13), NB: 0.66 (0.20), SVM: 0.66 (0.18)). This study shows the significant features for dengue diagnosis including fever, fever duration, headache, muscle and joint pain, nausea, vomiting, abdominal pain, shivering, malaise, loss of appetite, shortness of breath, rash, bleeding nose, bitter mouth, temperature and age. Conclusions. This beneficial information can help society in differentiating dengue from non-dengue diseases including malaria, typhoid fever, COVID-19 and other dengue-like symptoms diseases. This is pivotal to educate society to seek medical advice when dengue symptoms appear. Keywords: Dengue fever, Feature selection, Significant dengue features, Dengue prediction, Dengue diagnosis

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Section: Discussionmentioning

confidence: 99%

Determining important features for dengue diagnosis using feature selection methods

Bria,

Nani,

Siki

et al. 2024

Preprint

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“…These symptoms may last for a long time even after recovery. 3,4 Symptoms might linger for weeks or months, and some people never return to their previous level of health. 4 In this regard, the emergence of COVID-19 has compelled governments worldwide to take different steps and decisions to prevent the pandemic from spreading quickly.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of Musculoskeletal, Neurological and Physical Disorder After COVID-19 in Saudi Arabia: A Cross Sectional Study

Mahmoud,

Ebid,

Alghamdi

et al. 2023

F1000Res

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Background: The global pandemic of SARS-CoV-2, or COVID-19 continues to attack all human systems. Although COVID-19 is a respiratory disease, various extra-pulmonary manifestations, including musculoskeletal and neuropathies/myopathies was reported. This study aimed to investigates the long-term impacts of COVID-19 infection on physical health, capability of daily life activities, musculoskeletal and neurological functions in the Kingdom of Saudi Arabia (KSA). Methods: A total of 499 adults recovered from COVID-19 infection of both sexes, who resided in the KSA were recruited randomly and invited to participate in this cross-sectional web-based survey. A self-administered structured questionnaire was used as an instrument of data collection. All respondents returned the questionnaire. Their responses were recorded, stored into a Microsoft Excel sheet 2010 and analyzed with the Statistical Package for the Social Sciences (SPSS) version 24. Percentages were used to convey descriptive data. The percentages were presented with a 95% confidence interval (CI). For statistical significance, a 0.05 p-value was used. Results: The overall prevalence of neurological and musculoskeletal disorders as follows: headache (63.1%), muscle ache or weakness (62.3%), vertigo (25%), concentration problems (21.8%), breathing troubles (20.4%), loss of balance (19.4%), seizure (1%), and Guillain-Barre Syndrome (0.6%). The results also revealed a significant association between the influence of COVID-19 infection and daily activities, gender and respiratory disorders. Conclusion: The findings highlighted and concluded that COVID-19 infection had an impact on respiratory, nervous, musculoskeletal systems and affect daily activities.

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“…Pulmonary symptoms are the main cause of COVID-19-related rheumatic fever. It may also lead to enteric pathogens, which can bring on gastrointestinal issues including nausea, vomiting and diarrhoea [2].…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier

Jinu Paulson Siluvai Rathinam

2023

IJRITCC

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Early identification of COVID-19 diseased persons is crucial to avoid and prevent the transmission of the SARS-CoV-2 virus. To achieve this, lung Computed Tomography (CT) scan segmentation and categorization models have been broadly developed for COVID-19 diagnosis. Amongst, Multi-Scale function learning with an Attention-based UNet and Marginal Space Deep Ambiguity-attentive Transfer Learning (MS-AUNet-MSDATL) framework is developed to concurrently segment the COVID-19 infected regions and classify their risk levels from the CT/Chest X-Ray (CXR) scans. This model utilizes Convolutional Neural Network–Long Short Term Memory (CNN-LSTM) as a classifier for proper recognition. Although CNN-LSTM efficiently learns the spatial and temporal data, but it highly ignores the pixels and their adjacent information which results in lower classification rate. So, in this paper, Bidirectional Encoder Representations from Transformers (BERT) is introduced along with CNN-LSTM in MS-AUNet-MSDATL model to resolve the above mentioned issues for efficient COVID-19 risk level classification. Initially, the segmented CT and CXR images from MS-AUNet is given as input to the BERT model. BERT structure consist of stack of transformer encoder layers which extracts fixed features from a pre-trained models to obtain the numerical representation of the given image. Then, the numerical expressions from the BERT are transformed to pre-learned CNN model for selecting the important features from the given representations. The LSTM model receives the CNN output and generates a new representation based on the data order. In addition, Fully Connected Layer (FCL) maps the CNN-LSTM results into categorization classes to learn pixels and their adjacent information for COVID-19 risk level detection and diagnosis. The complete work is termed as MS-AUNet-EMSDATL. Finally, the test findings shows that the MS-AUNet-MS-B-DATL achieves accuracy of 98.67% and 98.55% on CT and CXR images compared to the other existing frameworks.

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Clinical Signs and Symptoms Associated with COVID-19: A Cross Sectional Study

Cited by 5 publications

References 15 publications

Determining important features for dengue diagnosis using feature selection methods

Determining important features for dengue diagnosis using feature selection methods

Prevalence of Musculoskeletal, Neurological and Physical Disorder After COVID-19 in Saudi Arabia: A Cross Sectional Study

Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier

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