An innovative ensemble model based on deep learning for predicting COVID-19 infection

Hybrid grey assisted whale optimization based machine learning for the COVID-19 prediction

Recently, COVID-19 (coronavirus) has been a huge influence on the socio and economic field. COVID-19 cases are seriously increasing day-day and also don't identified proper vaccine for COVID-19. Hence, COVID-19 is fast spreading virus and it causes more deaths. In order to address this, the work has proposed a machine learning (ML) scheme for the prediction of COVID-19 positive, negative, and deceased instances. Initially, the data is pre-processed by eliminating redundant and missing values. Then, the features are selected using hybrid grey assisted whale optimization algorithm (H-GAWOA). Finally, the classifier ANFIS (adaptive network-based fuzzy inference systems) is used for investigating the confirmed, survival and death rate of COVID-19. The performance is analysed on John Hopkins University dataset and the performances like MSE, RMSE, MAPE, and R2 are measured. In all the comparisons, the MSE value is very less for the proposed model. Particularly, in the deceased cases prediction, the MSE value is 0.00 for the proposed H-GAWOA-ANFIS. Finally, it is proved that the suggested model is able to generate the better results when contrast to the other approaches.

An interpretable machine learning framework for diagnosis and prognosis of COVID-19

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.