<![CDATA[Researchers have increasingly focused on applying machine learning algorithms to enhance healthcare operations in the past few years. Machine learning has become increasingly popular and has shown to be a viable strategy for raising the standard of healthcare, preventing disease transmission and early disease detection, reducing hospital operational expenses, aiding government healthcare programs, and enhancing healthcare efficiency. This review offers a succinct and well-structured summary of machine learning research that has been done in the field of healthcare. Specifically, the emphasis is placed on the examination of non-communicable illnesses, which pose a significant risk to public health and rank among the primary contributors to global mortality. Moreover, the COVID-19 pandemic, which is among the world's deadliest illnesses and has recently been formally declared a public health emergency, is included. This study aims to assist health sector researchers in choosing appropriate algorithms. After conducting a comprehensive investigation, it was shown that the Decision Tree (DT), Gaussian Naive Bayes (GNB), and Random Forest (RF), algorithms had the highest performance in healthcare classification, achieving a remarkable accuracy rate of 100%. In most tests, the Random Forest (RF) and Support Vector Machine (SVM) demonstrated consistently better performance]]>
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