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Effective Analysis and Predictive Model of Stroke Disease using Classification Methods

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 43 - Number 14
Year of Publication: 2012
Authors:
A. Sudha
P. Gayathri
N. Jaisankar
10.5120/6172-8599

A Sudha, P Gayathri and N Jaisankar. Article: Effective Analysis and Predictive Model of Stroke Disease using Classification Methods. International Journal of Computer Applications 43(14):26-31, April 2012. Full text available. BibTeX

@article{key:article,
	author = {A. Sudha and P. Gayathri and N. Jaisankar},
	title = {Article: Effective Analysis and Predictive Model of Stroke Disease using Classification Methods},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {43},
	number = {14},
	pages = {26-31},
	month = {April},
	note = {Full text available}
}

Abstract

In today's world data mining plays a vital role for prediction of diseases in medical industry. Stroke is a lifethreatning disease that has been ranked third leading cause of death in states and in developing countries. The stroke is a leading cause of serious, long term disability in US. The time taken to recover from stroke disease depends on patients' severity. Number of work has been carried out for predicting various diseases by comparing the performance of predictive data mining. Here the classification algorithms like Decision Tree, Naive Bayes and Neural Network is used for predicting the presence of stroke disease with related number of attributes. In our work, principle component analysis algorithm is used for reducing the dimensions and it determines the attributes involving more towards the prediction of stroke disease and predicts whether the patient is suffering from stroke disease or not.

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