ABSTRACT
In the current scenario, artificial intelligence (AI) and machine learning (ML) models play a major role in classification and prediction in business intelligence and early prediction of diseases. Machine learning plays a vital role in the medical industry, particularly when using medical databases to diagnose diseases. Much research is in progress using these techniques for the early prediction of diseases and to enhance medical diagnostics. In order to treat patients more effectively, predictive analysis with the aid of competent machine learning algorithms makes it possible to detect disease with more accuracy. Furthermore, implementing machine learning algorithms can result in quick and accurate disease prediction. The major goal of this research is to analyze how well different supervised machine learning algorithms perform in predicting COVID-19 using patient data. The purpose of this study is to provide an overview of machine learning techniques including naive Bayes, support vector machine, k-nearest neighbor, decision tree, and random forest that facilitate the categorization and prediction of various diseases. Generally, supervised machine learning prediction algorithms are created by learning a dataset where the label has been previously determined, making it possible to predict the result of instances that have not yet been assigned a label. This work focuses on machine learning algorithms used for disease prediction based on various performance metrics accuracy, F-score, precision, and recall. In general, any classification and prediction in business intelligence or any disease prediction in medical diagnosis is an essential and interesting work in recent years
