ABSTRACT
In COVID-19 is the most significant issue for the human community. The virus is easily converted into a new variant, which behaves differently from the previous one. Besides its changing behavior, its transmission and infection rate are very high which causes high death rate. It is a very challenging situation for the healthcare system to early diagnosis of diseases so that predict the transmission growth of virus the number of new, confirmed, recovered, and dead cases can be reduced. To deal with these issues, some prediction tools are required which can help to test and find the cause of existing cases so that it can help the effective and rapid arrangement to overcome the pandemic. To address this issue, we propose a symptom-base Recommendation System which are tested over the dataset by applying the concept of Machine Learning algorithms. In this work, we test our proposed system by suing various machine learning algorithm like LR, SVM, Navie Bays,KNN,Random Forest etc. The experimental results reveal that the proposed system is capable to diagnose the disease accurately approximate 99%. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.