An AI-enabled bias-free respiratory disease diagnosis model using cough audio

Ijaz, A., Saeed, T., Sadiq, I., Qureshi, H. N., Rizwan, A. and Imran, A. (2024) An AI-enabled bias-free respiratory disease diagnosis model using cough audio. Bioengineering, 11(1), 55. (doi: 10.3390/bioengineering11010055)

Abstract

Abstract 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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sadiq, Dr Ismail and Imran, Professor Ali
• Authors: Ijaz, A., Saeed, T., Sadiq, I., Qureshi, H. N., Rizwan, A., and Imran, A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Bioengineering
• Publisher: MDPI
• ISSN: 2306-5354
• ISSN (Online): 2306-5354
• Published Online: 05 January 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in Bioengineering 11(1): 55
• Publisher Policy: Reproduced under a Creative Commons License