Ischemic attack and cerebrovascular accident prediction analysis using machine learning approaches

In this article, a method has been proposed to predict stroke on earlier basis to prevent any critical damage or death caused by it. The aim is to predict stroke in earlier basis so the necessary steps can be taken to stave off any critical damage. For this purpose, the parameters like heart disease, smoking habit, hypertension, glucose level, body mass index level, age, and work pressure have been considered. Moreover, previously happened stroke is also being considered to observe and empathize the situations and based on these facts real time stroke prediction analysis using deep learning and machine learning approaches has been suggested. At the present time “machine learning” and “deep learning” tactics are put on for finding the problems’ solutions in “computer vision”, forecasting and “convolutional neural network”. This work proposes a before time prediction of stroke using various machine learning methodologies with occurrences of parameters based on which different classifiers are trained and data processing is done which attains a better outcome than other methodologies.