The incredible growth in medical technologies has increased in accumulation of loads of data in various forms. Application of medical informatics techniques and tools transform data into various forms of stuff which are sutable for mining. Implementing data mining techniques on clinical data enable the discovery of priceless knowledge from the huge collection of information stored. This study aims to conduct a review systematically on the classification techniques applied on clinical data from the perspective of (i) Medicine and (ii) Health care. The outcomes of this study indicate that maximum amount of research was published in the years 2015 and 2016. In medical data mining research, the most popular algorithm used was decision tree. Elsevier was identified as the leading publisher, which has published plenty of articles in this domain. 75% of the articles belonged to the category ‘medicine’ and rest of the articles belonged to the category ‘health care’. Out of the 75% articles, most of them were related to prognosis and diagnosis of diseases and fewer studies have been conducted in treatment recommendation. Choosing the best therapy and identifying the ideal treatment plan is a challenging task in case of diseases like heart failure and cancer. Moreover there is insufficient machine-learning research conducted in kidney diseases especially in chronic kidney disease and end-stage renal disease which are considered a global threat nowadays.

Classification, Data mining, Decision tree

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