[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121713-en":3,"doc-seo-121713-105":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},121713,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System - Journal Article","This study designs a computer-based clinical decision support system to enable earlier and more accurate decisions that can help prevent nontransmissible respiratory diseases. The work extracts discriminative pathological respiration features from recorded lung sounds and performs crackle and wheeze classification using machine learning. Time-frequency analysis and Mel-frequency cepstral coefficients (MFCC) support feature analysis and data conversion, while PCA reduces feature dimensionality. Experiments use ICBHI-2017 with 126 patients and 920 chest sound annotations, and models MusicANN, VGGish, and OpenL3 achieve 72%, 81%, and 69% accuracy.","VAWKUM Transactions on Computer Sciences [http://vfast.org/journals/index.php/VTCS@ 2023 ISSN](http://vfast.org/journals/index.php/VTCS@ 2023 ISSN)(e): 2308-8168, ISSN(p): 2411-6335  \nVolume 11, Number 1, January-June 2023 pp: 67-78  \nKeywords: Clinical Decision Support System, Crackle and Wheeze Detection, Artificial Intelligence, Machine Learning.  \nJournal Info:  \nSubmitted: January 08, 2023 Accepted:  \nMarch 12, 2023 Published:  \nMarch 18, 2023  \nDetection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System  \nSyed Waqad Ali1,2* , Muhammad Asif1 , Munaf Rashid1 , Sania Tanvir2 , Sarmad Shams3 , Sidra Abid2  \n1 Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan;  \n2 Department of Biomedical Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan; 3 Department of Biomedical Engineering, IBET, Liaquat University of Medical and Health Sciences, Jamshoro, Pakistan  \nAbstract This study aims to design a computer-based clinical decision support system that will help clinicians and healthcare personnel to make an early and correct decision to prevent the patient from nontransmissible respiratory diseases. The main contribution of this study is to analyze, investigate, and extraction of the useful feature of pathological respiration and Classification of Crackle and Wheeze from recorded lungs sound by using machine learning techniques. In the particular spectrogram, Time-frequency and Mel-Frequency cepstral coefficient (MFCC)technique is applied for feature analysis and data conversion into a format that can be useful for feature extraction and training models. PCA dimensional reduction technique is used to relatively reduced the dimension of the obtained feature. In order to apply various machine learning techniques a widely used dataset freely available dataset ICBHI-2017 is used. The respiratory lungs sound is comprised of 126 patients with 920 Chest sound annotations that include adventitious sounds such as “Crackle” and “Wheeze”. Machine learning algorithms such as MusicANN, VGGish, and OpenL3 were applied for testing the better accuracy of the classification model. The accuracy of the utilized classifier with the extracted feature set is determined as 72%, 81%, and 69% respectively.  \n*Correspondence Author Email Address:  \n[swaqad@zu.edu.pk](swaqad@zu.edu.pk) ;[swaqad@ssuet.edu.pk](swaqad@ssuet.edu.pk)  \nVAWKUM Transactions on Computer Sciences  \n1 Introduction  \nRespiratory diseases (RDs) are major public health issues worldwide that include chronic obstructive pulmonary disease (COPD), e.g., emphysema and chronic bronchitis, pneumoconiosis, and bronchiectasis, which cause a significant amount of social and economic burdens over societies [1] . Compared to other nontransmissible diseases, such as diabetes, cardiac disease, tumors, or cancer, Respiratory Diseases are neglected [1] . The significant risk factors for RDs include tobacco use, exposure to outdoor and indoor pollutants, allergens, obesity, occupational exposure, unhealthy diet, and physical inactivity. The aging population to tests more vulnerable to RDs, and the increase in exposure to the risk factors makes it a major problem. However, the epidemiology and disease problems of RDs vary substantially worldwide. The Forum of International Respiratory Societies reported that about 544 million people suffer from COPD and asthma globally [2–4] . The WHO report said that COPD was the third leading cause of death in 2017, behind cardiovascular diseases and neoplasms. Each year, 1.6 million people die due to lung cancer, tuberculosis kills 1.4 million people, while pneumonia and COPD also kill t of people globally [3? ]  \nRespiratory or pulmonary diseases are among the leading causes of death and disability. The condition worsens in third-world countries like Pakistan, where health facilities are limited [5] . Therefore, a significant amount of budget and research efforts h","cbCains0nzzFNTXN","https://ap.wps.com/l/cbCains0nzzFNTXN","pdf",255189,1,12,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the need for earlier and correct clinical decisions for patients at risk of nontransmissible respiratory diseases using lung-sound analysis.\"},{\"question\":\"Which signal processing and feature extraction methods are used?\",\"answer\":\"The approach applies time-frequency analysis and MFCC for feature analysis, and uses PCA for dimensionality reduction of the extracted features.\"},{\"question\":\"Which dataset and classification models are used, and what accuracy is achieved?\",\"answer\":\"Experiments use the ICBHI-2017 dataset with 126 patients and 920 chest sound annotations. The models MusicANN, VGGish, and OpenL3 achieve accuracy of 72%, 81%, and 69%, respectively.\"}]","Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System - Journal Article | PDF",1785806434,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detection-of-crackle-and-wheeze-in-lung-sound-using-machine-learning-technique-for-clinical-decision-support-system-journal-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detection-of-crackle-and-wheeze-in-lung-sound-using-machine-learning-technique-for-clinical-decision-support-system-journal-article/121713/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study targets the need for earlier and correct clinical decisions for patients at risk of nontransmissible respiratory diseases using lung-sound analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which signal processing and feature extraction methods are used?",{"text":80,"@type":76},"The approach applies time-frequency analysis and MFCC for feature analysis, and uses PCA for dimensionality reduction of the extracted features.",{"name":82,"@type":73,"acceptedAnswer":83},"Which dataset and classification models are used, and what accuracy is achieved?",{"text":84,"@type":76},"Experiments use the ICBHI-2017 dataset with 126 patients and 920 chest sound annotations. 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