An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio

• Published in: Bioengineering (Basel) 2024-01, Vol.11 (1), p.55
• Main Authors: Saeed, Tabish, Ijaz, Aneeqa, Sadiq, Ismail, Qureshi, Haneya Naeem, Rizwan, Ali, Imran, Ali
• Format: Article
• Language: English
• Subjects: Acoustics; Artificial intelligence; Artificial neural networks; Asthma; Bias; Bronchitis; Business metrics; Care and treatment; Causes of; Classification; Confounding (Statistics); confounding variables; Cough; COVID-19; Crowdsourcing; Data collection; Datasets; Deep learning; Diagnosis; Forecasts and trends; Generative adversarial networks; Infections; Long short-term memory; Machine learning; Medical diagnosis; Model accuracy; Model testing; Neural networks; Pneumonia; Prediction models; Respiratory diseases; Respiratory distress syndrome; Respiratory tract diseases; Smartphones; spectrograms; Test sets; Tuberculosis; Whooping cough
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11010055

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.