Data mining is a large collection of data into knowledge. It is a process of discovering interesting patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the Web, other information repositories, or data that are streamed into the system dynamically. In data mining, classification is an important function that assigns items in a collection to target categories or classes. The goal of the classification is to accurately predict the target class for each data points. It is a very important technique where large data are classified to retrieve relevant information. There are several classification techniques are available, which includes decision tree algorithm, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm, adaboost, random forest algorithm and fuzzy logic techniques. This paper proposes the survey of various classification techniques in data mining for healthcare. It also compares the classification techniques and produces the result based on the accuracy level.

Data mining, Classification, Decision tree, Bayesian networks, Genetic algorithm

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