  Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is
expanding quickly. Because errors in medical diagnostic systems might lead to
seriously misleading medical treatments, major efforts have been made in recent
years to improve computer-aided diagnostics applications. The use of machine
learning in computer-aided diagnosis is crucial. A simple equation may result
in a false indication of items like organs. Therefore, learning from examples
is a vital component of pattern recognition. Pattern recognition and machine
learning in the biomedical area promise to increase the precision of disease
detection and diagnosis. They also support the decision-making process's
objectivity. Machine learning provides a practical method for creating elegant
and autonomous algorithms to analyze high-dimensional and multimodal
bio-medical data. This review article examines machine-learning algorithms for
detecting diseases, including hepatitis, diabetes, liver disease, dengue fever,
and heart disease. It draws attention to the collection of machine learning
techniques and algorithms employed in studying conditions and the ensuing
decision-making process.
