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Intelligent Cardiovascular disease prediction using Data mining Techniques
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| Author:
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NITHYA S,, RAJASEKHARA BABU M
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| Abstract:
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Data mining is a technique which is helpful in identify the pattern in the large dataset. There are many techniques
available to work on a huge dataset. But Data mining having much higher advantages comparatively to solve the
real world problem. Healthcare mining is an important broad area of data mining and it is considered as one of
the important research areas. The classification and Prediction model gives real challenges. To solve this
challenges the classification techniques Logistic Regression and Neural network is commonly used.LR conveys
that there are one or many independent variables that will determine the problem output. Neural network looks
like the human being brain. NN has a collection of neurons. This neurons process the information and transmitting
to other neurons. This Paper focus on the feature selection methods similar to forwarding selection and backward
elimination using mean evaluation. ANN and LR applied on feature selection methods using Cross-validation
sample (CVS) and Percentage Split (PS) as a test option. From the experimental result, it is identified that heart
disease dataset using percentage split prediction accuracy of 89.99% is achieved by using 13 attributes with 303
instances for Neural Network as a highest.
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| Keyword:
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Cardiovascular disease, Feature selection, Health mining, Logistic regression, Neural Network.
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2018.10.04.150
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| Download:
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