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Context: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. Objective: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

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Parkinson's disease is a brain condition that induces difficulty walking, standing, concentrating, trembling, and weakness. Parkinson's symptoms typically begin slowly and increase with time. Whenever the condition develops, individuals can experience trouble walking and communicating to others. Old people mostly tend to suffer from this disease and the number is expected to increase in the future. Machine learning (ML) techniques could help in the medical field in processing and analyzing data that offer good solutions in this field in terms of high accuracy and less required time compared to conventional methods. In this study, we proposed an enhanced methodology based on utilizing SMOTE to balance the dataset, due to the available dataset is imbalanced. then adopted extra tree classifier with k-fold technique after we balanced the dataset with SMOTE. we have achieved the best accuracy with respect to the classification accuracy in the literature, the obtained accuracy of our proposed model was higher than the used approaches in the related works. The new model for classifying the Parkinson's disease-dataset with class-imbalance data distribution achieved an accuracy of 96.52% by using our proposed method. The result shown that the dataset is lacked of balancing and it proves that the balancing in the dataset is important specially in medical classification. The impact of Optimal function selection, either automated by PCA or manually carried out, is clearly still being studied, and plays an essential role in improving the performance of machine learning.