Performance Analysis of Logistic Regression, KNN, SVM, Naïve Bayes Classifier for Healthcare Application During COVID-19

ABSTRACT

Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36 s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address these challenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.