Enhancing Cardiovascular Risk Prediction with Explainable AI using Clinical Data

Abstract

Diabetes is a major risk factor for the development of cardiovascular issues which contribute to cardiovascular disease (CVD) being a leading cause of mortality worldwide. However, traditional machine learning methods are not widely adopted in healthcare systems because they lack interpretability, which is important for early and accurate CVD risk prediction and for ruling out effective clinical intervention. In this research, a hybrid architecture is proposed that incorporates diabetes related datasets as well as explainable artificial intelligence (XAI) methodologies that could improve the prediction power and transparency of the models. The proposed approach combines different datasets at the level of features and includes rigorous data pre-processing to detect metabolic and cardiovascular risk factors. Some of the significant clinical parameters are age, BMI, glucose, cholesterol, and blood pressure. These are standardized to create a single dataset which may be utilized for predictive modelling. The employment of two XAI approaches, SHAP (SHapley Additive Explanations) with tree-based ensemble models and integrated gradients with transformer based topologies, ensures both performance and interpretability. The technique improves confidence and usefulness in clinical settings by offering accurate predictions and explanations for the model’s judgments that are relevant to the circumstance. It is also utilized for visual investigation of clinical correlations of diabetes and cardiovascular disease and identify crucial risk variables. The results suggest that merging explainability approaches with powerful machine learning can considerably boost early identification and risk assessment. The proposed approach contributes to enhanced healthcare decision-making, offering a scalable, interpretable and dependable solution for cardiovascular disease prediction.