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Computer-aided diagnosis (CAD), a vibrant medical imaging research field, isexpanding quickly. Because errors in medical diagnostic systems might lead toseriously misleading medical treatments, major efforts have been made in recentyears to improve computer-aided diagnostics applications. The use of machinelearning in computer-aided diagnosis is crucial. A simple equation may resultin a false indication of items like organs. Therefore, learning from examplesis a vital component of pattern recognition. Pattern recognition and machinelearning in the biomedical area promise to increase the precision of diseasedetection and diagnosis. They also support the decision-making process'sobjectivity. Machine learning provides a practical method for creating elegantand autonomous algorithms to analyze high-dimensional and multimodalbio-medical data. This review article examines machine-learning algorithms fordetecting diseases, including hepatitis, diabetes, liver disease, dengue fever,and heart disease. It draws attention to the collection of machine learningtechniques and algorithms employed in studying conditions and the ensuingdecision-making process.

Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges