An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough
  Audio: A Case Study for COVID-19
Tabish Saeed Aneeqa Ijaz Ismail Sadiq Haneya N. Qureshi Ali Rizwan Ali Imran
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
(RBFNet), an end to end solution that effectively mitigates the impact of
confounders in the training data distribution. RBFNet ensures accurate and
unbiased RD diagnosis features, emphasizing its relevance by incorporating a
COVID19 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
RBFNet. An additional bias predictor is incorporated in the classification
scheme to formulate a conditional Generative Adversarial Network (cGAN) which
helps in decorrelating the impact of confounding variables from RD prediction.
The merit of RBFNet is demonstrated by comparing classification performance
with 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