Complexity-driven feature selection for enhancing tuberculosis detection

Most existing machine learning approaches for tuberculosis (TB) screening typically utilize large, high-dimensional acoustic feature sets without examining their intrinsic discriminative power. To address this gap, we introduce a complexity-based feature selection approach that evaluates temporal and spectral descriptors using Fisher score (F1), class-distribution overlap (F2), and Shannon entropy (F4). Applied to the CODA-TB dataset (9772 audio recordings from 1105 participants), the proposed method identified 7 highly informative features from the original 26 features, primarily consisting of mel-frequency cepstral coefficients (MFCC) derivatives and spectral-shape measures. The resulting model achieved performance comparable to full-feature baselines while reducing feature dimensionality by 73% and computational cost by up to 14×. Comparative evaluation against four established feature selection techniques, supported by ablation and statistical analyses, confirmed the efficiency and robustness of the complexity-driven strategy, with no statistically significant loss in performance. These findings highlight the potential of lightweight, interpretable, and computationally efficient models for TB cough-based screening in resource-constrained environments.