In today&apos;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&apos; 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. -Kohn, L. T. , Corrigan, J. M. , and Donaldson, M. S. , to err is human: building a safer health system. Institute of Medicine (IOM).
A. Sudha, et al REF have proposed a principle component analysis algorithm was used for reducing the dimensions and that have been determines the attributes involving more towards the prediction of stroke disease and predicts whether the patient suffering from stroke disease or not.
Effective Analysis and Predictive Model of Stroke Disease using Classification Methods
International Journal of Computer Applications
Computer Science