A sustainable advanced artificial intelligence-based framework for analysis of COVID-19 spread

The idea of sustainability aims to provide a protected operating environment that supports without risking the capacity of coming generations and to satisfy their demands in the future. With the advent of artificial intelligence, big data, and the Internet of Things, there is a tremendous paradigm transformation in how environmental data are managed and handled for sustainable applications in smart cities and societies. The ongoing COVID-19 (Coronavirus Disease) pandemic maintains a mortifying impact on the world population's health. A continuous rise in the number of positive cases produced much stress on governing organizations worldwide, and they are finding it challenging to handle the situation. Artificial Intelligence methods can be extended quite efficiently to monitor the disease, predict the pandemic's growth, and outline policies and strategies to control its transmission or spread. The combination of healthcare, along with big data, and machine learning methods, can improve the quality of life by providing better care services and creating cost-effective systems. Researchers have been using these techniques to fight against the COVID-19 pandemic. This paper emphasizes on the analysis of different factors and symptoms and presents a sustainable framework to predict and detect COVID-19. Firstly, we have collected a data set having different symptoms information of COVID-19. Then, we have explored various machine learning algorithms or methods: including Logistic Regression, Naive Bayes, Decision Tree, Random Forest Classifier, Extreme Gradient Boost, K-Nearest Neighbour, and Support Vector Machine to predict and detect COVID-19 lab results, using different symptoms information. The model might help to predict and detect the long-term spread of a pandemic and implement advanced proactive measures. The findings show that the Logistic Regression and Support Vector Machine outperformed from other machine learning algorithms in terms of accuracy; algorithms exhibit 97.66% and 98% results, respectively.