A Heterogeneous Ensemble Learning Framework for Detecting Acute Viral Respiratory Infections via Multisource Data Fusion

Acute viral respiratory infections (AVRIs), including COVID-19 and SARS, present ongoing public health challenges. Traditional diagnostic methods often rely on limited inputs—typically cough or speech sounds—rarely incorporate self-reported symptoms, and still require clinical visits or medical imaging, leading to higher costs and increased exposure risks. To support affordable, contact-free selftesting, we introduce the heterogeneous ensemble framework with multisource data fusion mechanism. This framework integrates respiratory, cough, and speech audio data with self-reported information. Within this system, deep learning models are employed to extract and analyze audio features for estimating infection probability, while conventional machine learning classifiers handle the selfreported data. Finally, predictions from both data types are combined using a meta-learning strategy to enhance overall detection performance. Experiments on COVID-19 detection validate the proposed framework, and the interpretability analysis reveals the critical predictive features and classifiers. This multisource fusion strategy also provides a transferable foundation for detecting other AVRIs.