Abstract
COVID-19 remains a significant global public health challenge. While nucleic acid tests, antigen tests, and CT imaging provide high accuracy, they face inefficiencies and limited accessibility, making rapid and convenient testing difficult. Recent studies have explored COVID-19 detection using acoustic health signals, such as cough and breathing sounds. However, most existing approaches focus solely on audio classification, often leading to suboptimal accuracy while neglecting valuable prior information, such as clinical symptoms. To address this limitation, we propose SympCoughNet, a deep learning-based COVID-19 audio classification network that integrates cough sounds with clinical symptom data. Our model employs symptom-encoded channel weighting to enhance feature processing, making it more attentive to symptom information. We also conducted an ablation study to assess the impact of symptom integration by removing the symptom-attention mechanism and instead using symptoms as classification labels within a CNN-based architecture. We trained and evaluated SympCoughNet on the UK COVID-19 Vocal Audio Dataset. Our model demonstrated significant performance improvements over traditional audio-only approaches, achieving 89.30% accuracy, 94.74% AUROC, and 91.62% PR on the test set. The results confirm that incorporating symptom data enhances COVID-19 detection performance. Additionally, we found that incorrect symptom inputs could influence predictions. Our ablation study validated that even when symptoms are treated as classification labels, the network can still effectively leverage cough audio to infer symptom-related information.