Prediction of COVID-19 Severity Using Patient's PHR

A significant health crisis, including the current COVID-19 outbreak, presents us for an opportunity to think about it and focus on how we may improve the way we handle health care in the future to make us humans better prepared and capable of dealing with such an incident.Since the COVID-19 trend has swayed irregularly, they have remained in the dark, unsure how much resources they will have even in the future week.At these instances, difficult period to be capable of predicting exactly what sort of resources a person have it necessary now of a positive test, or perhaps even earlier, would take place extremely beneficial to organizations, as they will be able to get or make preparations with their resource required to save that patient's existence.The aim of the work is to devise a system that would be both outlay and reliable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Develop a Model for Assessing the Most Efficient Diseases Diagnosis using Machine Learning

so, machine learning techniques are being developed to improve performance and maintenance prediction. Increasing our knowledge of the relationship between humans and algorithms, Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Numerous researchers recently developed numerous computer-aided diagnostic algorithms employing various supervised learning approaches. Early identification of sickness may help to reduce the number of people who die as a result of these illnesses. Using machine learning techniques, this research creates an efficient automated illness diagnostic algorithm. We chose three key disorders in this paper: coronavirus, cardiovascular diseases, and diabetes. The data are inputted into a mobile application in the suggested model, the investigation is then done in a real-time dataset that used a pre-trained model machine learning technique trained within the same dataset then implemented in firebase, and lastly, the illness identification result can be seen in the mobile application. Logistic regression is a method of prediction calculation © 2022 IEEE.

Data wrangling for virtual attendance : A conceptual model

With the onset and increase in the number of online courses and blended mode of delivery owing to the Covid-19 pandemic, there has been a need for an accurate and consolidated online attendance registers that can easily be queried at Botswana Accountancy College. As the attendees switch access devices or when the experience connectivity issues during a remotely conducted class, multiple entries of the same attendee are logged to the Microsoft Teams attendance registers. Additionally, the format of the downloaded attendance registers is different from the current attendance management systems, making attendance accounting a cumbersome task. A data wrangling model therefore will assist in the restructuring of the downloaded registers by eliminating duplicates, reshaping the same for ease of querying and consolidation. The success of the wrangling model will simplify attendance reporting, free-up the lecturers of the manual task of attendance accounting and coincidentally eliminating the human error element.