Abstract

Disease diagnosis is the most important concern in the healthcare field. Machine Learning (ML) classification approaches can greatly improve the medical industry by allowing more accurate and timely disease diagnoses. Recognition and machine learning promise to enhance the precision of diseases assessment and treatment in biomedicine. They also help make sure that the decision-making process is impartial. This paper looks at some machine learning classification methods that have remained proposed to improve healthcare professionals in disease diagnosis. It overviews machine learning and briefly defines the most used disease classification techniques. This survey paper evaluates numerous machine learning algorithms used to detect various diseases such as major, seasonal, and chronic diseases. In addition, it studies state-of-the-art on employing machine learning classification techniques. The primary goal is to examine various machine-learning processes implemented around the development of disease diagnosis and predictions.