Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Mansour, Nehal A.; Saleh, Ahmed I.; Badawy, Mahmoud; Ali, Hesham

doi:10.1007/s12652-020-02883-2

Cited by 56 publications

(81 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 3 depicts the SHAP summary plot for one of the LightGBM classifiers in the cascade level of the DF-COVID-19 model. In this example, the selected features are set to be the same as the study 60 . The SHAP summary plot reveals the impacts of input features on COVID-19 positive cases prediction.…”

Section: Feature Importancementioning

confidence: 99%

“…In this section the DF-COVID-19 model is evaluated by comparing it with other state-of-the-art approaches 13,27,[60][61][62][63][64] . For comparison fairness, both DF-COVID-19 and previous studies used the same dataset.…”

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

“…Thus, the confusion matrix values provide evidence for the robustness of the proposed model. In the third experiment, the performance of DF-COVID-19 is compared with the strategy in the study 60 . The dataset used in this experiment is taken from San Raffaele Hospital in Milan 10 .…”

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

“…The dataset used in this experiment is taken from San Raffaele Hospital in Milan 10 . The Feature Correlated Naïve Bayes (FCNB) is introduced in 60 to diagnosis COVID-19 in an accurate and fast way. FCNB has four phases: Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP).…”

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

“…The Feature Selection Phase (FSP) selects the most important features based on a wrapper method of genetic algorithm. The study 60 reported the results of two scenarios which are: the FSGA and the FCNB strategy. Table 4 shows the comparison between DF-COVID-19, FSGA and FCNB.…”

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

See 4 more Smart Citations

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

AlJame

Imtiaz

Ahmad

et al. 2021

Preprint

View full text Add to dashboard Cite

The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and poses considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

show abstract

Section: Feature Importancementioning

confidence: 99%

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

Section: Comparison With Other Classification Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

AlJame

Imtiaz

Ahmad

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

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

View full text Add to dashboard Cite

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

show abstract

COVID‐19: A systematic review of prediction and classification techniques

Varma,

Kalra,

Kirmani

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

COVID‐19 has affected more than 760 million people all over the world, as per the latest record of the WHO. The rapid proliferation of COVID‐19 patients not only created a health emergency but also led to an economic crisis. An early and accurate diagnosis of COVID‐19 can help in combating this deadly virus. In line with this, researchers have proposed several machine learning (ML) and deep learning (DL) techniques for detecting COVID‐19 since 2020. This article presents currently available manual diagnosis methods along with their limitations. It also provides an extensive survey of ML and DL techniques that can support medical professionals in the precise diagnosis of COVID‐19. ML methods, namely K‐nearest neighbor, support vector machine (SVM), artificial neural network, decision tree, naive bayes, and DL methods, viz. deep neural network, convolutional neural network (CNN), region‐based convolutional neural network, and long short‐term memories, are explored. It also provides details of the latest COVID‐19 open‐source datasets, consisting of x‐ray and computed tomography scan images. A comparative analysis of ML and DL techniques developed for COVID‐19 detection in terms of methodology, datasets, sample size, type of classification, performance, and limitations is also done. It has been found that SVM is the most frequently used ML technique, while CNN is the most commonly used DL technique for COVID‐19 detection. The challenges of an existing dataset have been identified, including size and quality of datasets, lack of labeled datasets, severity level, data imbalance, and privacy concerns. It is recommended that there is a need to establish a benchmark dataset that overcomes these challenges to enhance the effectiveness of ML and DL techniques. Further, hurdles in implementing ML and DL techniques in real‐time clinical settings have also been highlighted. In addition, the motivation noticed from the existing methods has been considered for extending the research with an optimized DL model, which attained improved performance using statistical and deep features. The optimized deep model performs better than 90% based on efficient features and proper classifier tuning.

show abstract

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Cited by 56 publications

References 74 publications

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

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

COVID‐19: A systematic review of prediction and classification techniques

Contact Info

Product

Resources

About