  Addressing heart failure (HF) as a prevalent global health concern poses
difficulties in implementing innovative approaches for enhanced patient care.
Predicting mortality rates in HF patients, in particular, is difficult yet
critical, necessitating individualized care, proactive management, and enabling
educated decision-making to enhance outcomes. Recently, the significance of
voice biomarkers coupled with Machine Learning (ML) has surged, demonstrating
remarkable efficacy, particularly in predicting heart failure. The synergy of
voice analysis and ML algorithms provides a non-invasive and easily accessible
means to evaluate patients' health. However, there is a lack of voice
biomarkers for predicting mortality rates among heart failure patients with
standardized speech protocols. Here, we demonstrate a powerful and effective ML
model for predicting mortality rates in hospitalized HF patients through the
utilization of voice biomarkers. By seamlessly integrating voice biomarkers
into routine patient monitoring, this strategy has the potential to improve
patient outcomes, optimize resource allocation, and advance patient-centered HF
management. In this study, a Machine Learning system, specifically a logistic
regression model, is trained to predict patients' 5-year mortality rates using
their speech as input. The model performs admirably and consistently, as
demonstrated by cross-validation and statistical approaches (p-value < 0.001).
Furthermore, integrating NT-proBNP, a diagnostic biomarker in HF, improves the
model's predictive accuracy substantially.
