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Impact of modifiable and non-modifiable risk factors on the prediction of stroke disease

The entire paper portrays the way in which the occurrence of stroke is predicted by modifiable and non-modifiable factors such as diabetes mellitus (DM), hypertension, gender, age of the patients. The proposed prototype describes how these factors have a great impact on the prediction of stroke. The working protocol acquires details of patients through their case sheets from Sugam Multispeciality Hospital, as a part of data collection and it is preprocessed to overcome redundancy. As a next step the processed data is fed into various machine learning algorithms to fetch the evaluation results of various risk factors. The outcome of the entire working protocol determines that more than 50% of the patients are affected by stroke because of its incidence with risk factors such as DM, hypertension, above 40 years of age and its predominant more towards the male gender than female.