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Our regular way of life has been disrupted by the COVID-19, and we have been obliged to accept the procedures that are in place under the new normal regime. It is envisaged that the standard diagnostic technique will evolve throughout the course of the procedure. As a help to this type of diagnostic technique, our research group is developing a tool. In this article, the group discusses the importance of employing two diagnostic metrics that have proven to be pivotal in many diagnoses for doctors, and how they might be used to their advantage. Together, natural language processing-based symptoms measures and a machine learning-based strategy that takes into account medical vitals can help to minimise the error percentage of detection by as much as 50%. The technique suggested in this study is the first of its type, and the authors have obtained findings that are satisfactory in terms of accuracy. A further justification for suggesting such a strategy is the manner in which a fusion algorithm might arrive at the correct results from two concurrent algorithms performing the same task. One of the group's other objectives was to give the doctor a valuable opinion in the form of such an architectural design. The suggested design may be employed at any point of care facility without the need for any additional infrastructure or escalation of the current amenities to accommodate the proposed architecture.

A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics’ everyday workflows could help physicians make better and more personalised decisions.

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