Prediction of Brain Stroke Risk Using Machine Learning and Deep Learning: An Hybrid XGBoost-ResNet-18 CDSS

Abstract

Stroke continues to be one of the major causes of global deaths and neurologic impairment. Conventional clinical scoring system such as CHADS₂ scores static and additive in nature and unable to process high dimensional multi-modal data. This paper proposes a hybrid artificial intelligence (AI) based clinical decision support system (CDSS) for evaluating brain stroke risk based on a combination of structured clinical indicators with medical images. The hybrid model comprises of an XGBoost classifier trained on tabular biomarkers such as hypertension status, average glucose level and body mass index (BMI), and a ResNet-18 deep learning model using transfer learning to recognize stroke indicators from MRI/CT scans. With a view to addressing the black-box nature inherent in medical AI applications, we incorporate Explainable AI (XAI) techniques such as SHAP (SHapley Additive exPlanations) to provide an interpretation at the feature level for each prediction. Our prototype utilizes a decoupled React 18 – Flask architecture with PostgresSQL database back end, JSON Web Token (JWT) based rolebased authentication and a forensics traceable audit trail with automatic generation of clinical PDF reports.