Multistage Classification And Ensemble Learning Techniques For Automated Respiratory Sound Analysis: A Comprehensive Review

Abstract

Background

Respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, and pneumonia, are leading causes of global morbidity and mortality. Early and accurate diagnosis remains challenging, especially in resource-limited settings. Automated respiratory sound classification, supported by machine learning and deep learning, has emerged as a non-invasive and cost-effective diagnostic alternative. This systematic review examines the role of multistage classification frameworks and ensemble learning techniques in improving the accuracy, robustness, and clinical applicability of respiratory sound analysis.

Methods

A comprehensive literature search was conducted across major scientific databases, focusing on studies employing multistage, machine learning, or ensemble-based approaches for respiratory sound classification. Eligible studies were screened based on predefined inclusion criteria related to pulmonary acoustics, feature extraction, signal processing, and classification models. Extracted data were synthesized to compare methodologies, feature engineering strategies, ensemble algorithms, model architectures, benchmark datasets (ICBHI 2017, PhysioNet), and performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.

Results

Findings indicate that multistage classification pipelines significantly enhance model reliability by integrating sequential preprocessing, feature extraction, dimensionality reduction, and classifier fusion. Ensemble learning methods—including bagging, boosting, random forests, stacking, and deep hybrid models—consistently outperform single-stage classifiers in handling noisy, non-stationary respiratory sounds and imbalanced datasets. Deep learning models such as convolutional neural networks and transformer-based architectures show superior performance when combined with advanced feature representations. However, challenges persist related to dataset variability, limited annotations, lack of standardization, and insufficient real-time or wearable-device compatibility. Explainable AI approaches are increasingly incorporated to support clinical interpretability.

Conclusion

Multistage and ensemble learning techniques demonstrate strong potential to improve automated respiratory sound classification and support early diagnosis of respiratory diseases. Despite promising results, advancements are needed in dataset standardization, multimodal integration, self-supervised learning, and IoT-enabled real-time monitoring. Future research should prioritize clinically interpretable, scalable, and regulation-ready AI systems to enable widespread deployment in respiratory healthcare.