Explainable quantum-enhanced machine learning for hypertension prediction

Abstract Chronic disease prediction presents ongoing challenges in healthcare, primarily due to the complexity of medical data and the need for models that are both accurate and interpretable. This study introduces a quantum-enhanced machine learning model specifically designed for the prediction of hypertension, combining quantum feature transformation with classical algorithms to deliver precise and reliable results. The model demonstrates high performance, achieving an accuracy of 98.40%, precision of 99.3%, recall of 98.6%, and an F1-score of 98.9%. To ensure transparency and facilitate clinical interpretation, explainable AI (XAI) techniques are employed through SHAP values, highlighting critical factors such as hypertension drug usage, age, ferritin, and cholesterol levels as key contributors to hypertension prediction. This quantum-based approach exemplifies the potential for leveraging cutting-edge technologies in healthcare, offering a robust solution that not only ensures predictive accuracy but also supports interpretability—essential for informed clinical decision-making. The integration of quantum computing and explainable machine learning represents a promising step forward in the development of predictive models tailored to complex medical datasets.