Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections.

The objective of this study was to develop and evaluate a non-invasive method for distinguishing patients with chronic obstructive pulmonary disease (COPD) from those with respiratory tract infections (RTI) using voice signal analysis and machine learning. Fixed-pattern voice signals were collected from 25 COPD patients and 25 RTI patients (serving as the control/comparison group). Multi-dimensional voice feature analysis was performed to identify features significantly differentiating the two groups. Statistically significant features were selected and subjected to dimensionality reduction. Logistic Regression (LR) and Random Forest (RF) models were then trained and evaluated for classification performance in distinguishing COPD from RTI. Over 400 voice features were initially analyzed. Eighteen features showed highly significant differences between COPD and RTI patients (P <; 0.05). In the task of distinguishing COPD patients from RTI patients, the LR model achieved a test set area under the curve AUC of 0.95, significantly outperforming the RF model (AUC = 0.76). This study demonstrates the feasibility of using voice analysis and machine learning, particularly the LR model, as a promising non-invasive tool for differentiating COPD from RTI. It provides a foundation for the practical application and further optimization of this voice-based approach in clinical settings requiring differential diagnosis of respiratory conditions.