Explainable machine learning and social determinants of health in stroke prediction

Background: Current stroke prediction models, relying solely on traditional medical data, overlook the role of Social Determinants of Health (SDoH) like socioeconomic status and education. This narrow focus can lead to inaccurate predictions, potentially exacerbating healthcare disparities and hindering the development of effective preventive measures. This work investigates the role of SDoH in stroke and how incorporating SDoH data into AI models can improve stroke prediction, ultimately empowering healthcare providers with a more holistic view of patient risk for better decision-making and equitable healthcare delivery.Research Objectives:1. To improve the performance of stroke prediction AI models by integrating SDoH into these models.2. To ensure transparency and interpretability in stroke prediction through the application of explainable AI (XAI) methodologies.Method: The study employs datasets from the Institut de la statistique du Québec that include both clinical indicators (e.g. diabetes, heart disease, weight) and SDoH (e.g.economic, neighbourhood conditions). We applied seven machine learning models (Random Forest), Gradient Boosting Machine (GBM), CatBoost (CB), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Neural Networks (NN), and K-Nearest Neighbors (KNN) alongside XAI techniques to investigate the role SDoH plays in the models’ predictive performances. XAI methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were implemented, shedding light on the influence of SDoH in the algorithms’ predictions. Performance of models was evaluated using standard metrics such as accuracy, precision, recall, F1 score and AUC (Area under the curve).Results: Our study investigated the impact of incorporating SDoH data into stroke prediction models. SDoH data variably improved performance depending on the model and specific SDoH factors incorporated, illustrating its important role alongside traditional medical data in assessing stroke risk. Our LGBM model showed maximum improvement on incorporation of SDoH features where its accuracy improved by 11.2% (from 65.9% to 77.1%). The inclusion of demographic, economic, and personal SDoH factors were the most influential. XAI methods revealed self-perceived health and stress levels as key factors for stroke prediction, emphasizing the importance of personal well-being in stroke assessment. Notably, the Light Gradient Boosting Machine (LGBM) model achieved the best performance, demonstrating an Area Under the Curve (AUC) of 81%. This translates to accuracy of 77.6%, precision of 78.6%, recall of 75.5%, and F1 score of 77.0%, showcasing LGBM’s proficiency in handling the complex relationships within SDoH data. These findings suggest the importance and potential of SDoH-integrated AI models for improved stroke prediction.Conclusion: Our findings highlight the role of SDoH data in building accurate and equitable healthcare models. Integrating SDoH factors improve stroke prediction accuracy by 1% to 3%, and foster fairer and more comprehensive patient risk assessments by considering the broader social and environmental influences on health. Furthermore, XAI techniques provide deeper insights into how SDoH and other factors contribute to predictions, promoting transparency and interpretability in these AI-driven solutions. This transparency is essential for building trust and ensuring ethically sound decision-making in healthcare

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