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Supervised Machine Learning Approaches for Medical Data Set Classification - A Review

Clinical decision making, using medical expert systems, is a complex task as it requires more accuracy. Hence the design of such medical expert systems requires relevant and the most suitable machine learning algorithm. This paper reviews the various supervised machine learning classification approaches available along with their functional use in medical field. A number of classification algorithms are considered and reviewed for their relative performances and practical usefulness on different types of health care datasets. This review gives an inference that the performance of the classification technique will depend on the features of the dataset that is analyzed with more emphasis on the health care dataset. While keeping the classification accuracy and speed as major criteria of this study, it is inferred that the SVMs and Neural Networks are more suitable for medical dataset classification with higher performance.