Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

Authors

  • Sangeetha Balasubramanian Department of Instrumentation and Control Engineering, National Institute of Technology, Tamil Nādu, India
  • Periyasamy Rajadurai Department of Instrumentation and Control Engineering, National Institute of Technology, Tamil Nādu, India

DOI:

https://doi.org/10.46604/ijeti.2023.12294

Keywords:

cepstral coefficients, discrete wavelet transform, machine learning classifiers, pulmonary diseases, variational mode decomposition

Abstract

    The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings.

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Published

2023-12-29

How to Cite

[1]
Sangeetha Balasubramanian and Periyasamy Rajadurai, “Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds”, Int. j. eng. technol. innov., vol. 14, no. 1, pp. 85–102, Dec. 2023.

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Articles