Predicting Heart Disease Risk in Diabetic patients using a Pipeline of Ensemble Learning and XAI - Enhanced Approaches

An innovative approach for examining the complex interaction between diabetes and heart disease is presented in "A Unified Approach to Diabetes and Heart Disease Analysis: An Ensemble of XAI-Enhanced Models". For this project, explainable Artificial Intelligence (XAI) approaches are applied to strengthen a unified ensemble of AI models. The method encourages interpretability and openness in the examination of these common health concerns. By utilizing heterogenous data sources, like clinical notes, lifestyle information, and medical pictures, the ensemble model provides thorough insights. Critical variables and risk markers that underlie the co-occurrence of diabetes and heart disease are revealed by integrating machine learning models with XAI. Early risk assessment and tailored interventions are made possible by this strategy, which has the potential to completely transform the healthcare industry. Improving the models' interpretability seeks to close the trust and confidence gap that exists between sophisticated AI systems and real-world medical applications, benefiting both patients and healthcare providers. The aim is to improve early risk assessment and diagnosis through interpretable insights and improved predictive accuracy through the integration of multiple AI models and XAI methods. This will ultimately improve the quality of healthcare decision-making in these crucial domains amidst the various conditions and unknown stages of heart health.