MFCC-Chroma Fusion with Demographic-Aware Random Forest for Lung Sound Disease Classification

Auscultation-based lung disease detection remains subjective and inconsistent. This study proposes two complementary approaches: a dual-channel CNN-LSTM using MFCC-Chroma fusion, and a Random Forest model incorporating patient demographic data, both evaluated on the ICBHI 2017 dataset. The CNN-LSTM models reached up to 80% accuracy, while demographic features also proved valuable for classification, offering competitive performance in accuracy, robustness, and class-wise fairness compared to prior work in the field.