Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers

作者：Yi Lyu, Quan-Cheng Jiang, Shuai Yuan, Jing Han Hong, Hung-Chih Chen, Haimei Wu, Yiqin Wang, Yujing Shi, Haixia Yan, Jin Xu · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-14645-1 · 被引用次数：10 · 研究领域：Phonocardiography and Auscultation Techniques、Music and Audio Processing、Respiratory and Cough-Related Research

Traditional diagnostic methods for asthma, a widespread chronic respiratory illness, are often limited by factors such as patient cooperation with spirometry. Non-invasive acoustic analysis using machine learning offers a promising alternative for objective diagnosis by analyzing vocal characteristics. This study aimed to develop and validate a robust classification model for adult asthma using acoustic features from the vocalized /ɑː/ sound. In a case-control study, voice recordings of the /ɑː/ sound were collected from a primary cohort of 214 adults and an independent external validation cohort of 200 adults. This study extracted features using a modified extended Geneva Minimalistic Acoustic Parameter Set and compared seven machine learning models. The top-performing model, Extreme Gradient Boosting, was further assessed through ten-fold cross-validation, external validation, and feature analysis using SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations. The Extreme Gradient Boosting classifier achieved the highest performance on the test set, with an accuracy of 0.8514, an Area Under the Curve of 0.9130, a recall of 0.8804, a precision of 0.8387, an F1-score of 0.8567, a Kappa coefficient of 0.7018, and a Matthews Correlation Coefficient of 0.7071. On the external validation set, the model maintained strong performance with an accuracy of 0.8100, AUC of 0.8755, recall of 0.8300, precision of 0.7981, F1-score of 0.8137, Kappa of 0.6200, and Ma...