Brain Stroke Prediction through Deep Learning Techniques with ADASYN Strategy

A stroke occurs when there is a sudden interruption of blood flow to a specific area of the brain. Brain cells perish and lose functionality when deprived of blood flow, depending on the specific region of the brain impacted. Early diagnosis of symptoms could provide useful insights for supporting a healthy life and predicting strokes. Considerable endeavours have been made to enhance stroke prevention and management due to the profound impact strokes have on populations. This paper presents a hybrid deep learning model to predict the risk of an early-stage brain stroke. To evaluate the model’s effectiveness, a benchmark dataset for stroke prediction was selected through the online Kaggle platform. The dataset was cleaned and normalised using a variety of preprocessing techniques in order to predict strokes. LSTM, RNN, CNN, and GRU models were among the models used in this study to perform classification tasks. The results demonstrated that CNN+GRU attained 98.78% accuracy and LSTM+RNN attained 98.23% accuracy rate. The empirical findings also exhibited that deep learning techniques were more effective than approaches reported in state of the art.