ABSTRACT
COVID-19 respiratory viral disease has changed the entire research communities' focus towards Diseases, Health Care, Treatment and related resources decision making and services. Fuzzy approach gives simplification and improvement in processing logic and speed applied to Data analytics and testing of algorithm for different business purposes may be future research domain. Significant COVID-19 analytics can help to classify and prioritize the resources the future consequences to take enhanced self-ruled decisions, to recognise and design pattern in data spread to design strategic policies for medical, health care units and stakeholders.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
Pattern and Group behavior of COVID-19 patient's data is very much important aspect of data analysis for medical stakeholders to frame decision strategies and to design routine set-ups. PCA is old mathematical machine learning grouping analysis technique but popular in recently for data analysis in grouping insights, so the researcher has implemented principal component analysis to study dominance in data with varied component grouping with proportional variation and also used graphical visualizations. The strength of PCA implementation is to maintain real valued data with dimension reduction but without loss of key information. The experiment result of COVID-19 datais showing component 1 contributing to highly positive testResult that is having High impact on patients. PCA shows relative dimensions analysis. PCA dimensions plotted shows independence in dimension with 900 angle dimensions in data spread. The results identified for COVID-19 data glimpse with dominant component formation. Copyright © 2022 Ubiquity Press. All rights reserved.
ABSTRACT
COVID-19 respiratory viral disease has changed the entire research communities' focus towards Diseases, Health Care, Treatment and related resources decision making and services. Fuzzy approach gives simplification and improvement in processing logic and speed applied to Data analytics and testing of algorithm for different business purposes may be future research domain. Significant COVID-19 analytics can help to classify and prioritize the resources the future consequences to take enhanced self-ruled decisions, to recognise and design pattern in data spread to design strategic policies for medical, health care units and stakeholders. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
The entire globe is facing the pandemic and it has devastated the life of society. On the grounds of diverse medical facilities and variants of COVID 19 cases the proposed solution provides in depth analysis of incidents generated information, which is information of COVID 19 patients which maximizes benefits to health organizations and also maximizes value relationship with its medical stakeholders. This gives rise a thirst for carrying out the study on the available data analysis of COVID 19 data and use of some machine learning techniques to support decision making by identifying patterns using fuzzy classifications, dominance analysis by using Principle component analysis and to provide machine learning based predictions on available data for COVID 19 cases specifically for severity level and level of recurrence with respect to different input parameters(Age, time of diagnosis, pre-existing conditions, and symptoms). So, the study focuses to identify relationships, patterns and dominance which will support COVID 19 medical research field. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
As COVID-19 Pandemic and related data is very recent. COVID-19 infected most of the population from entire globe with different impact. It disclosed the limitation of access of health and care resources. Various parameters like different symptoms, different existing health conditions, different age, diagnosis level and great uncertainties made the condition vaguer. Fuzzy can handle such vagueness and uncertainty of such voluminous data of patients and can support to medical stakeholders, experts, hospitals, pharmaceuticals etc. Fuzzy Logic is widely used to address so many uncertainties, incompleteness or imprecision. The current experiment implements Fuzzy Inference System for pattern identification and classification by applying fuzzy approach with Fuzzy Logic in R for performance improvement. This focuses on designing Fuzzy Rule base, Model and inference for COVID 19 data analysis.
ABSTRACT
In this uncertainty of COVID 19 pandemic, everyone want to be safer and there is no choice than to adopt new normal, so it is sound to analyse pre and post COVID relative impact an patients health so as to redesign routine or new normal. In fact, all these patients and such patients from entire world are facing post COVID problems also which may be related to different parameters different symptoms, age, preexisting conditions, infection frequency, severity level, stage of diagnosis and more specifically life span and treatment styles in various Hospitals at diverse socio-economic structures/though processes. Hence the medical staff and stakeholders necessarily have to adopt a distinct strategy for future treatments based on past experiences. COVID treatment in hospitals need to be understood as the systematic and coordinated implementation of medical policy by Medical undertakings whether private or state owned, local or regional, national or international, level to achieve the optimum cost of life, time and resources of the needs of particular patient groups and, in doing so, to achieve an appropriate treatments and care.