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.