Speaker
Description
Heart failure (HF) is a chronic disease all around the world, affecting millions of people. Despite recent advances in the diagnosis of heart failure, the traditional diagnosis is highly invasive and time-consuming. The disease results in physiological changes in patients, including increased vocal edema in the vocal tract and accumulation of fluid in the lungs, thereby affecting the laryngeal and respiratory systems. These physiological changes ultimately affect the voice patterns that can be detected using artificial intelligence techniques. Moreover, heart failure patients exhibit more pauses during speech, with increased voice hoarseness due to congestion and early exhaustion during physical activity, including multiple voice-based activities. Here, we present a non-invasive approach to predict heart failure patients using voice biomarkers and features that capture changes in voice patterns compared with those of suspected heart failure patients. We compare and evaluate the performance of using five voice recordings per patient, each containing 94 extracted features, for a total of 470 features. The high-dimensional feature space is reduced using SHapley Additive exPlanations (SHAP) to select important features, and the results are compared with those from manually selected feature groups. Moreover, using top-k SHAP-based feature selection as a hyperparameter during cross-validation with varying k across folds, thereby affecting the final reported features’ frequency and stability, is also reported and compared with the fixed, appropriate top-k selection.