Integrating expert knowledge with machine learning for AI-based stroke identifications and treatment systems

Stroke is a leading cause of mortality and disability worldwide, requiring early detection and timely intervention to improve patient outcomes. However, in resource-limited locations, the lack of specialists often leads to delayed and inaccurate diagnoses. To address this, we propose an AI-driven stroke identification and treatment system that integrates expert knowledge with machine learning, enabling healthcare providers to make informed decisions without direct specialist input. The data for this study were obtained from Debre Berhan Referral Hospital through expert interviews, prescriptions, and from a public dataset in the Kaggle platform. Feature selection was performed using decision trees, Chi-Square tests, Elastic Net coefficients, and correlation analysis. Additionally, we applied to Shapley Additive Explanations to demonstrate the feasibility of feature selection in AI model development. Machine learning models, including Decision Tree, Random Forest, and Support Vector Machine, were evaluated, and Random Forest classifier achieved the highest accuracy of 99.4% using k-fold cross-validation technique. Expert knowledge was encoded in Prolog, while machine learning models were implemented in Python to develop a hybrid expert system. Medical professionals evaluated the system, confirming its effectiveness as a decision-support tool for stroke diagnosis and treatment. This approach demonstrates the potential of AI-driven expert systems to enhance stroke management, particularly in regions with limited access to specialized care.