Automatic Cough Analysis for Non-Small Cell Lung Cancer Detection

2025-07-25

AI Summary

This study investigates non-invasive early pre-screening of non-small cell lung cancer (NSCLC) via automated cough audio analysis. We propose a multi-model classification framework integrating traditional machine learning (SVM, XGBoost) and deep learning approaches (CNN, VGG16 with transfer learning), and—novel for this task—incorporate SHAP for model-agnostic interpretability and feature attribution. Additionally, we systematically evaluate model fairness across age and sex subgroups. Experimental results show that the CNN achieves 83% test accuracy, while SVM demonstrates practical utility in low-compute settings. Fairness analysis reveals marginally greater performance disparity across age groups than across sex groups. This work establishes a new paradigm for community-deployable, low-cost, non-invasive, and interpretable NSCLC pre-screening, supported by empirical validation on real-world cough audio data.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Developing automatic cough analysis for NSCLC detection
• Evaluating machine learning models for cough classification
• Assessing model fairness across age and gender groups

Innovation

• Uses SVM and XGBoost for cough analysis
• Applies CNN and VGG16 transfer learning
• Enhances interpretability with SHAP explanations