Knowledge Patterns in Clinical Data through Data Mining: A Review on Cancer Disease Prediction | Abstract

ISSN ONLINE(2278-8875) PRINT (2320-3765)

Research Article Open Access

Knowledge Patterns in Clinical Data through Data Mining: A Review on Cancer Disease Prediction

Abstract

Data mining is an essential step in the process of knowledge discovery in databases in which intelligent methods are applied in order to extract patterns. Cancer research is generally clinical and/or biological in nature. Data driven statistical research has become a common complement. Predicting the outcome of a disease is one of the most interesting and challenging tasks with data mining applications. (The use of computers powered with automated tools)Large volumes of medical data are being collected and made available to the medical research groups. As a result, Knowledge Discovery in Databases (KDD), which includes data mining techniques, has become a popular research tool for medical researchers to identify and exploit patterns and relationships among large number of variables, and made enable them predict the outcome of a disease using the historical cases stored within datasets. The objective of this study is to summarise various reviews and technical articles on diagnosis and prognosis of cancer. It gives an overview of the current research being carried out on various cancer datasets using the data mining techniques to enhance cancer diagnosis and prognosis.

Ms. Pooja Agrawal, Mr. Suresh kashyap, Mr.Vikas Chandra Pandey, Mr. Suraj Prasad Keshri

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