AN INTELLIGENT SYSTEM FOR IDENTIFYING THE RISK OF COVID-19 USING LOGISTIC REGRESSION

Abstract: SARS-CoV-2, also known as Covid-19 Corona Virus, caused damage worldwide, and the situation is getting worse. Every day, it is an epidemic disease from one person to another. Therefore, it is important to keep track of the number of patients involved. The current system provides computer data in an integrated way that is very difficult to analyze and predict the growth of disease locally and globally. To overcome this difficulty, Machine learning algorithms can be used effectively to map out the disease as well as continue to solve this problem. By analyzing X-ray images of the patient’s chest, machine learning, which is part of computer science, is important in classifying patients appropriately about illness. Supervised machine learning models with support algorithms (e.g. LR, SVR, and Time-series algorithms) for data analysis to back up classification helps model training to predict total global value confirmed cases or who will be at risk of contracting the disease in the coming days. Total Global data collection is being processed, pre-processed, and the number of verified cases has arrived at a specific date is issued, which is used as a model-set training in this regard proposed work. Supervised machine learning algorithms are used for training a model for predicting the growth of cases in the coming days. In this paper, we have proposed a method to identify whether a patient has a risk of covid-19 using a machine learning framework-logistic regression model, considering multiple symptoms and, also developed a web page that displays the attributes, and sample records, graphs related to the at risk-patients of covid-19.