Disease Prediction using Data Mining

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Data mining is defined as sifting through very large amounts of data for useful information. Some of the most important and popular data mining techniques are association rules, classification, clustering, prediction and sequential patterns. Data mining techniques are used for variety of applications. In health care industry, data mining plays an important role for predicting diseases. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of test should be reduced. This reduced test plays an important role in time and performance. This technique has an advantages and disadvantages. This research paper analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting heart disease, Diabetes and Breast cancer.

Data Mining is the process of extracting hidden knowledge from large volumes of raw data. The knowledge must be new, not obvious, and one must be able to use it. Data mining has been defined as “the nontrivial extraction of previously unknown, implicit and potentially useful information from data. It is “the science of extracting useful information from large databases”. It is one of the tasks in the process of knowledge discovery from the database.

Data Mining is used to discover knowledge out of data and presenting it in a form that is easily understand to humans. It is a process to examine large amounts of data routinely collected. Data mining is most useful in an exploratory analysis because of nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers.

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