Responding to Challenge Call of Machine Learning Model Development in Diagnosing Respiratory Disease Sounds

TL;DR

This paper presents a machine learning model that automatically detects respiratory sounds like coughing and sneezing, aiding early diagnosis of respiratory diseases, using datasets from environmental sound classification and multiple classification techniques.

Contribution

Developed a novel machine learning approach for respiratory sound classification using MFCC features and SVM, achieving high accuracy in disease diagnosis support.

Findings

SVM with RBF kernel achieved 83% accuracy.

Effective feature extraction from environmental sound datasets.

Multiple classification methods evaluated for respiratory sound detection.

Abstract

In this study, a machine learning model was developed for automatically detecting respiratory system sounds such as sneezing and coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the "Pfizer digital medicine challenge" on the "OSFHOME" open access platform. "Environmental sound classification" called ESC-50 and AudioSet sound files were used to prepare the dataset. In this dataset, which consisted of three parts, features that effectively showed coughing and sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. Three different classification techniques were considered to perform successful respiratory sound classification in the dataset containing more than 3800 different sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, ensemble aggregation and decision tree classification methods were used as classification techniques. In an attempt to classify coughing and sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success.