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Prognostication of STROKE using Data Mining Classification Techniques
Abstract
Mining provides useful information from the huge volume of the data stored in respositories. Stroke is the rapid loss of brain function due to disturbance in the blood supply to the brain. Computerizer tomographic scan images have been used for diagnosis of stroke and its subtypes. The patient risk level is classified using datamining classification technique such as Naive bayes, KNN, Random forest etc., The collected datasets are pre-processed and then used for implementing this algorithms using performance measured like ROC, Kappa statistics, RMSE and MAE. This paper gives the survey about different classification techniques used for predicting the risk level of each person. Finally, the random forest was selected as the stroke predicting algorithm because of its higher accuracy.
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