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

arXiv: https://arxiv.org/abs/2111.14354 · HTML: https://mlsanity.com/paper/2111.14354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 77 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition](https://mlsanity.com/paper/2007.00144/) (2020)
- [A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels](https://mlsanity.com/paper/2111.05895/) (2021)
- [An Ensemble of Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2007.07966/) (2020)
- [Urban Sound Tagging using Convolutional Neural Networks](https://mlsanity.com/paper/1909.12699/) (2019)
- [A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection](https://mlsanity.com/paper/2008.07695/) (2020)
- [Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound](https://mlsanity.com/paper/2102.04880/) (2021)
- [PANACEA cough sound-based diagnosis of COVID-19 for the DiCOVA 2021 Challenge](https://mlsanity.com/paper/2106.04423/) (2021)

## Abstract (arXiv)

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.