A Systematic Method of Stroke Prediction Model based on Big Data and Machine Learning

Abstract

There is an enormous increase in number of diseases worldwide. The non-communicable diseases such as cardio vascular disease will leads to death. The second major reason of death in people worldwide occurs due to stroke. It affects any portion of brain due to interruption or reduction of Blood supply. The brain damage can be reduced if required actions taken earlier. So there is necessary requirement to build stroke predictive models. The combined techniques of Machine Learning (ML) and Deep Learning (DL) techniques play the vital role in Disease Prediction. There are many researches has been done for stroke prediction using various ML Algorithms. In order to improve accuracy, the proposed model will work on the hybrid ANNRF (Artificial Neural Network-Random Forest). The proposed method can be reached 94% in classification accuracy.