Automatic Cough Analysis for Non-Small Cell Lung Cancer Detection

• URL: http://arxiv.org/abs/2507.19174v1
• Date: Fri, 25 Jul 2025 11:30:22 GMT
• Title: Automatic Cough Analysis for Non-Small Cell Lung Cancer Detection
• Authors: Chiara Giangregorio, Cristina Maria Licciardello, Vanja Miskovic, Leonardo Provenzano, Alessandra Laura Giulia Pedrocchi, Andra Diana Dumitrascu, Arsela Prelaj, Marina Chiara Garassino, Emilia Ambrosini, Simona Ferrante,
• Abstract summary: Early detection of non-small cell lung cancer (NSCLC) is critical for improving patient outcomes.<n>We explore the use of automatic cough analysis as a pre-screening tool for distinguishing between NSCLC patients and healthy controls.<n>Recordings were analyzed using machine learning techniques, such as support vector machine (SVM) and XGBoost.
• Score: 33.37223681850477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early detection of non-small cell lung cancer (NSCLC) is critical for improving patient outcomes, and novel approaches are needed to facilitate early diagnosis. In this study, we explore the use of automatic cough analysis as a pre-screening tool for distinguishing between NSCLC patients and healthy controls. Cough audio recordings were prospectively acquired from a total of 227 subjects, divided into NSCLC patients and healthy controls. The recordings were analyzed using machine learning techniques, such as support vector machine (SVM) and XGBoost, as well as deep learning approaches, specifically convolutional neural networks (CNN) and transfer learning with VGG16. To enhance the interpretability of the machine learning model, we utilized Shapley Additive Explanations (SHAP). The fairness of the models across demographic groups was assessed by comparing the performance of the best model across different age groups (less than or equal to 58y and higher than 58y) and gender using the equalized odds difference on the test set. The results demonstrate that CNN achieves the best performance, with an accuracy of 0.83 on the test set. Nevertheless, SVM achieves slightly lower performances (accuracy of 0.76 in validation and 0.78 in the test set), making it suitable in contexts with low computational power. The use of SHAP for SVM interpretation further enhances model transparency, making it more trustworthy for clinical applications. Fairness analysis shows slightly higher disparity across age (0.15) than gender (0.09) on the test set. Therefore, to strengthen our findings' reliability, a larger, more diverse, and unbiased dataset is needed -- particularly including individuals at risk of NSCLC and those in early disease stages.

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