In general humans are said to be social animals. In the huge expanded internet, it's really difficult to detect and find out useful information about a medical illness. In anticipation of more definitive studies of a causal organization between stroke risk and social network, It would be suitable to help social individuals to detect the risk of stroke. In this work, a DRFS methodology is proposed to find out the various symptoms associated with the stroke disease and preventive measures of a stroke disease from the social media content. We have defined an architecture for clustering tweets based on the content using Spectral Clustering an iterative fashion. The class label detection is furnished with the use of highest TF-IDF value words. The resultant clusters obtained as the output of spectral clustering is prearranged as input to the Probability Neural Network (PNN) to get the suitable class labels and their probabilities. Find Frequent word set using support count measure from the group of clusters for identify the risk factors of stroke. We found that the anticipated approach is able to recognize new symptoms and causes that are not listed in the World Health Organization (WHO), Mayo Clinic and National Health Survey (NHS). It is marked that they get associated with precise outcomes portray real statistics. This type of experiments will empower health organization, doctors and Government segments to keep track of stroke diseases. Experimental results shows the causes preventive measures, high and low risk factors of stroke diseases.