[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120874-en":3,"doc-seo-120874-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120874,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds - Decision-making tool for respiratory diagnosis","Computer-based automated system for classifying pulmonary diseases from real-time lung sound recordings collected in hospital settings. The pipeline applies signal denoising using discrete wavelet transform and variational mode decomposition to enhance classifier performance, then extracts cepstral features including Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients. Four supervised machine learning models—decision tree, k-nearest neighbor, linear discriminant analysis, and random forest—are evaluated with accuracy, recall, specificity, and F1 score. The random forest achieves 99.72% accuracy with perfect recall, specificity, and F1, and supports diagnosis, especially in resource-limited environments.","International Journal of Engineering and Technology Innovation, vol. x, no. x, 20xx, pp. xx-xx  \nMachine Learning-Based Classification of Pulmonary Diseases through  \nReal-Time Lung Sounds  \nSangeetha Balasubramanian*, Periyasamy Rajadurai  \nDepartment of Instrumentation and Control Engineering, National Institute of Technology, Tamil Nādu, India  \nReceived 24 May 2023; received in revised form 14 August 2023; accepted 15 August 2023  \nDOI: [https://doi.org/10.46604/ijeti.2023.12294](https://doi.org/10.46604/ijeti.2023.12294)  \nAbstract  \nThe 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.  \nKeywords: cepstral coefficients, discrete wavelet transform, machine learning classifiers, pulmonary diseases, variational mode decomposition  \n1. Introduction  \nPulmonary diseases have emerged as a significant cause of the highest mortality in society. World Health Organization (WHO) categorizes the “big five” respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), acute lower respiratory tract infections, lung cancer, and tuberculosis, are responsible for causing the deaths of over 3 million individuals globally each year. These respiratory diseases share identical symptoms, such as adventitious breathing, which can complicate the diagnostic procedure. Due to their severe consequences, an early and precise diagnosis of these types of diseases has become crucial [1] .  \nDiagnosis of pulmonary illnesses can be done clinically in a variety of ways. Imaging techniques like chest X-rays, computer tomography scans, and magnetic resonance imaging are used to diagnose pulmonary diseases. Contrarily, adopting these imaging modalities presents several difficulties, including the risk of repeated exposure to harmful radiation, the expense of equipment, and the challenge of deploying these methods in remote areas. A spirometer is a common technique used to diagnose lung function. It measures the air inhaled and exhaled by the lungs and identifies irregular breathing patterns. However, this technique has challenges such as the cooperation of patients in forced breathing, and requires a professional operator. Due to its high cost, this device can be used only in clinical settings, needs regular calibration, and is inefficient in detecting obstructive-restrictive abnormalities [2] .  \n* Corresponding author. E-mail address: [sangeetha27may@gmail.com](sangeetha27may@gmail.com)  \n[English language proofreader: Yen-Chun Hsieh](English language proofreader: Yen-Chun Hsieh)  \n2 International Journal of Engineering and Technology Innovation, vol. x, no. x, 20xx, pp. xx-xx  \nIn recent years, detecting pulmonary disease through lung sounds (LSs) has been an area of interest in bioinformatics. LSs provide valuable information about pulmonary diseases and can be heard throughout the posterior and anterior regions of the chest. Auscultation is a cost-effective, non-invas","cbCaipFLIfOr00y2","https://ap.wps.com/l/cbCaipFLIfOr00y2","pdf",3437425,1,18,"English","en",105,"# Introduction\n## Lung sound–based disease classification\n## Limitations of conventional diagnosis\n# Proposed approach\n## Denoising via VMD-DWT\n## Feature extraction and classifiers\n# Experimental setup and evaluation\n## Metrics and datasets","[{\"question\":\"Which lung sound features are used for pulmonary disease classification?\",\"answer\":\"The system uses cepstral features, including Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, to represent lung sound signals for classification.\"},{\"question\":\"How does the study improve classifier performance before classification?\",\"answer\":\"It applies denoising techniques, specifically discrete wavelet transform and variational mode decomposition, as a preliminary step to enhance signal quality.\"},{\"question\":\"Which classifier performs best and how is it evaluated?\",\"answer\":\"The random forest classifier outperforms the others, reaching 99.72% accuracy with 100% recall, specificity, and F1 score, evaluated using accuracy, recall, specificity, and F1 metrics.\"}]","Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds - 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