Medical diagnosis of COVID-19 using blood tests and machine learning

Chadaga, Krishnaraj; Prabhu, Srikanth; K, Vivekananda Bhat; Umakanth, Shashikiran; Sampathila, Niranjana

doi:10.1088/1742-6596/2161/1/012017

Cited by 16 publications

(25 citation statements)

References 14 publications

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“…For further evaluation of our proposed ensemble learning-based method, we benchmarked the results of our models with previous studies using the same datasets and the same performance metrics. The proposed model shows a significant improvement compared to the existing study using state-of-the-art methods [ 27 , 66 , 67 , 68 ] that applied a hybrid fuzzy interference engine and DNN, and a similar study by Brinati et al [ 27 ], which uses a three-way random forest classifier in the prediction of COVID-19 using the RT-PCR dataset. In another study, Chadaga et al [ 68 ] used SMOTE for oversampling, and then evaluated four machine learning algorithms (Random Forest, Logistic Regression, KNN, and Xgboost), while their hyperparameters were optimized using grid search.…”

Section: Resultsmentioning

confidence: 86%

“…The proposed model shows a significant improvement compared to the existing study using state-of-the-art methods [ 27 , 66 , 67 , 68 ] that applied a hybrid fuzzy interference engine and DNN, and a similar study by Brinati et al [ 27 ], which uses a three-way random forest classifier in the prediction of COVID-19 using the RT-PCR dataset. In another study, Chadaga et al [ 68 ] used SMOTE for oversampling, and then evaluated four machine learning algorithms (Random Forest, Logistic Regression, KNN, and Xgboost), while their hyperparameters were optimized using grid search. The best result in terms of accuracy was the 92% achieved with the Random Forest classifier.…”

Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

“…The summary of related studies with the description of the model type and performance metrics is shown in Figure 6. Our proposed model is compared with a Hybrid Fuzzy inference engine and deep neural network (HDS) approach [66], a three-way random forest classifier (TWFR) approach [27], and Random Forest (RF) [67,68]. evaluated four machine learning algorithms (Random Forest, Logistic Regression, KNN, and Xgboost), while their hyperparameters were optimized using grid search.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“… Comparison of results with previous studies. Our proposed model is compared with a three-way random forest classifier (TWFR) approach [ 27 ], Random Forest (RF) [ 67 ], SMOTE + RF [ 68 ], and a Hybrid Fuzzy inference engine and deep neural network (HDS) [ 66 ]. …”

Section: Figurementioning

confidence: 99%

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An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

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Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection

Saini,

Bhat Kasaragod,

Prakasha

et al. 2023

Concurrency and Computation

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SummaryA persistent, targeted cyber attack is called an advanced persistent threat (APT) attack. The attack is mainly launched to gain sensitive information, take over the system, and for financial gain, which creates nowadays more hurdles and challenges for the organization in preventing, detecting, and recovering from such attacks. Due to the nature of APT attacks, it is difficult to detect them quickly. Therefore machine learning techniques come into these research areas. This study uses deep and machine learning models such as random forest, decision tree, convolutional neural network, multilayer perceptron and so forth to categorize and effectively detect APT attacks by utilizing publicly accessible datasets. The datasets used in this study are CSE‐CIC‐IDS2018, CIC‐IDS2017, NSL‐KDD, and UNSW‐NB15. This study proposes the hybrid ensemble machine learning model, a mixed approach of random forest and XGBoost classifiers. It has obtained the maximum prediction accuracy of 98.92%, 99.91%, 99.24%, and 97.11% for datasets CSE‐CIC‐IDS2018, CIC‐IDS2017, NSL‐KDD, and UNSW‐NB15, with a false positive rate of 0.52%, 0.12%, 0.62%, and 5.29% respectively. These results are compared to other closely related recent studies in the literature. Our experiment's findings show that our model has performed significantly better for all datasets.

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DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images

Sharma

Mishra

2023

Neural Comput & Applic

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Medical diagnosis of COVID-19 using blood tests and machine learning

Cited by 16 publications

References 14 publications

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection

DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images

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