Brain stroke detection (SD) is one of the major chronic diseases which can be detected using the MRI images. Most of the severe patterns in the SD database are detected using the machine learning models. Most of the traditional machine learning models such as random tree, random forest, SVM, Naïve Bayes etc. are difficult to find the feature extraction and difficult to detect essential features for disease classification. Also, the classification of the SD patterns based on severity is difficult in the traditional models due to complexity in feature selection and ranking. Hence, it is essential to develop an appropriate and an effective automatic test in order to diagnose stroke for better disease management. However, as the number of stroke features increases, traditional models require high computational memory and time for feature selection and pattern evaluation. Also, these models generate high false positive rate and error rate due to high feature space and data uncertainty. In order to overcome these issues, a hybrid feature selection-based classification learning model is designed and implemented on high dimensional dataset. Experimental results proved that the present model improves the true positivity and error rate compared to the traditional models.