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 data is 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.