A Robust Automatic Algorithm for Statistical Analysis and Classification of Lung Auscultations

Chatterjee, Joyjit; Sharma, Garima; Sexena, Ayush; Mehra, Anu; Gupta, Varun

doi:10.1109/spin.2019.8711646

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…These algorithms assign class labels to the data based on knowledge derived from training data. In the literature, certain studies concentrate on identifying wheeze and crackle sounds in pulmonary records, often associated with unhealthy cases [11][12][13][14][15][16]. Other studies explore the classification of prevalent lung diseases such as pneumonia, asthma, and COPD [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, certain studies concentrate on identifying wheeze and crackle sounds in pulmonary records, often associated with unhealthy cases [11][12][13][14][15][16]. Other studies explore the classification of prevalent lung diseases such as pneumonia, asthma, and COPD [16][17][18][19][20][21][22][23]. Below, we provide an overview of both types of studies.…”

Section: Introductionmentioning

confidence: 99%

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A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases

Kababulut,

Gürkan Kuntalp,

Düzyel

et al. 2023

Diagnostics

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The aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values.

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