In today’s era there is a huge amount of data available with health care; however, the knowledge about the data is rather poor. So there is a need to process that enormous size of medical dataset instead of just storing extract valuable information or useful knowledge. Data mining is the process of extracting hidden knowledge from large volumes of raw data using techniques like statistical analysis, machine learning, clustering, neural networks and genetic algorithms. A logical combination of multiple pre-existing techniques or Hybrid algorithms for data mining to enhance performance and provide better results. Data mining is used to discover hidden patterns and relationships out of data and presenting it in a form that can be easily understood. Data mining plays an important role in disease prediction. Data Mining is used intensively in the medical field to predict diseases such as heart disease, diabetes, breast cancer etc. In this paper, a survey is carried out on several single and hybrid data mining approaches used for disease prediction.

Data mining, Data Mining Techniques, Hybrid Approach, Diseases

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