Stroke Probability Prediction from Medical Survey Data: AI-Driven Analysis with Insightful Feature Importance using Explainable AI (XAI)
Simon Bin Akter, Sumya Akter, Tanmoy Sarkar Pias · 2023
Prioritizing dataset dependability, model performance, and interoperability is a compelling demand for improving stroke probability prediction from medical surveys using AI in healthcare. These collective efforts are required to enhance the field of stroke probability assessment and demonstrate the transformational potential of AI in healthcare. This effective study leverages the CDC’s published 2021 BRFSS dataset to explore AI-based stroke probability prediction. Numerous substantial and notable contributions have been established from this study. To start with, the dataset’s dependability is improved through preprocessing and oversampling techniques that overcome the challenges of missing data and class imbalance. In order to identify the most promising models, eight different AI models are meticulously evaluated including DT, RF, GNB, RusBoost, AdaBoost, CNN, ANN, and MLP. The study combines top-performing models using fusion approaches such as soft voting, hard voting, and stacking to demonstrate the combined prediction performance. The stacking-based model combined with GNB, RF, and AdaBoost demonstrated superior performance, achieving a specificity of 74.06% and a sensitivity of 74.20%. The work also employs explainable AI (XAI) approaches to highlight the subtle contributions of important features across both healthy and stroke cases, improving model interpretability. The comprehensive approach to stroke probability prediction employed in this study enhanced dataset reliability, model performance, and interpretability, demonstrating AI’s fundamental impact in healthcare.