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ABSTRACT
The data mining is a process that is basically used to mine the data and give
the result that are hidden to the internal database. The data mining is done in very
formal that are basically used in medical field, engineering field and quite also in
technical field. The data mining basically uses the machine learning algorithm which
are predictable in nature. The heart disease prediction is basically a process which
took some of the information from the user and then mine the data to predict the
answer i.e, it has heart disease or not. Following are some data mining technique
that are used for the prediction. These are Random Forest Decision Tree & Nave
Bayes etc. from the algorithm procedure it is formed the Random Forest has the best
accuracy and precision with 81% when composed to other algorithm for heart
disease prediction.
Data Mining is the process of non-trivial extraction of implicit, previously
unknown and potentially useful information from data. A pattern is interesting if it is
valid for a given test data with some degree of certainty, novel, potentially useful and
easily understood by humans. The huge amount of data generated for prediction of
heart disease is too complex and voluminous to be processed and analysed by
traditional methods. Advanced Data Mining tools overcome this problem by
discovering hidden patterns and useful information from complex and voluminous
data. Researchers reviewed literature on prediction of heart disease using data
mining techniques and reported that Neural Network technique overcome all other
techniques with higher levels of accuracy. Applying Data Mining techniques on
healthcare data can help in predicting the likelihood of patients getting heart disease.
This paper highlights the important role played by data mining tools in analysing
huge volumes of healthcare related data in prediction and diagnosis of disease.
