A Manta-Ray Hill Climbing Vision Transformer Model for Predicting Ischemic Stroke Outcome

Abstract

An ischemic stroke attack can cause permanent damage to healthy brain tissue, leading to a permanent loss of motor or sensory function. It can also result in disability or death if not diagnosed and treated promptly. Early prediction of the outcome of the first stroke, such as disability or death, can help many patients by administering appropriate medications to save their lives. Additionally, early prediction of a recurrent stroke within 14 days of the initial stroke can contribute to prevent its recurrence. This paper first proposes a modified Manta-Ray Foraging Optimizer (MMRFO) to enhance the characteristics of the MRFO technique. This approach is based on incorporating the Hill Climbing methodology into the original MRFO in order to improve the exploitation phase, which is responsible for locating the promising zone in the search area. The proposed approach is then utilized to determine the appropriate hyperparameters of the Vision Transformer(ViT) model to predict stroke outcomes prior to its occurrence. To transform categorical data to numerical values, an ASCII encoder module is included. In the feature selection step, the Harris Hawk Optimization approach (HHO) is used to identify the most important elements that may define the stroke. A comparative study has been performed to confirm the effectiveness of the proposed methodology. The results demonstrate that the proposed technique with a Vision Transformer achieves superior results compared to state-of-the-art algorithms. The accuracy of the proposed technique was improved to 87% for the first dataset and 83% for the second, which is clearly superior to that of the other models and earlier research.