Smart Healthcare: IoMT-Based Detection and Classification of Respiratory Disorders

This paper presents a comprehensive approach for detecting respiratory diseases using IoMTs and Machine Learning (ML) algorithms, leveraging audio recordings from multiple sensor locations on the body. By capturing, extracting, and analyzing diverse audio features, such as MFCC, STFT, and Mel-spectrogram, we aim to detect and classify six respi-ratory conditions: Bronchiectasis, Bronchiolitis, COPD, Healthy, Pneumonia, and URTI. We used a publicly annotated dataset to conduct the experiment and analyze the performance of our proposed approach. This dataset underwent preprocessing, which included feature extraction, removal of rare diseases, data flattening, and encoding for model training. Our findings demonstrate that Deep Learning (DL), such as the Convolutional Neural Network (CNN) model achieved the highest accuracy of 92.4 % and an AU C of 97 %, highlighting its potential in audio-based diagnostics. Our experimental results prove that DL, particularly CNN, outperforms traditional ML techniques in detection accuracy, which makes them a good choice in developing non-invasive, efficient, and cost-effective solutions for respiratory disease detection.