Stroke Prediction Using Deep Learning and Transfer Learning Approaches

Stroke is one of the leading causes of death and disability worldwide. The ideal solution to the stroke problem is to prevent it in advance by controlling metabolic factors, atrial fibrillation, hypertension, smoking, Etc. However, unless the physiological indicators are abnormal, it is difficult for medical personnel to decide whether special precautions are necessary for a patient based solely on monitoring the potential patient. There was a great category imbalance between stroke and non-stroke patients, so this study tried to use various techniques to solve the problem of categorical unbalanced stroke prediction problem. Then, deep learning models were used to predict whether the patients would have a stroke. Finally, the classification experiment is carried out through transfer learning to observe whether the evaluation metrics are further improved. According to the experimental results, this study effectively reduced the false negative rate (FNR) and false positive rate (FPR) of stroke prediction and improved the overall accuracy of stroke prediction through the category imbalance treatment and deep learning method.