An Assessment for Predicting Stroke Patients using Bidirectional LSTM

Abstract

Stroke is the medical condition when the supply of blood to the brain is either interrupted or reduced for very certain duration of time. When this happens, the brain does not get enough oxygen or nutrients, and brain cells start to die. This thesis presents the development and evaluation of a machine learning model using deep learning techniques. The improved, memory based Bidirectional recurrent neural called Bidirectional Long short-term memory (BLSTM RNN) is used for the research work. The model thus developed predict whether a patient will experience stroke or not based on a time series input data computation. A 3-layer architecture having single BLSTM unit, Adam as model optimizer and dropout regularization of 0.42 achieves accuracy of 91%. The model is developed by processing patient time series information which includes demographic and medical historical data. It includes age, gender, hypertension, heart diseases, and altogether ten biometric information. This work contributes for decision support for individuals and medical persons on their future stroke possibility.