Early detection of heart failure using trend similarity metrics combined with hybrid deep learning based on real-world health monitoring data

Abstract Heart failure (HF) is a global health crisis, with over 18 million annual deaths attributed to cardiovascular diseases. Early detection is critical to enable timely lifestyle interventions and targeted pharmacotherapy, yet existing prediction methods often fail to integrate high-dimensional multi-source data or capture dynamic temporal patterns in patient health records. Here, we present a cross-disciplinary framework that combines trend similarity analysis (rooted in Haar wavelet decom