[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125062-en":3,"doc-seo-125062-105":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":20,"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},125062,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases - Artificial intelligence-based medicine article","Chronic Obstructive Pulmonary Disease (COPD) creates major, growing burdens on hospital care, making early detection and personalized management increasingly urgent. This research presents a novel machine-learning pipeline for classifying multiple COPD severities using auscultation lung sound data streams. Two open datasets with diverse acoustic and clinical characteristics are transformed into matrix eigenspace representations via decomposition to capture discriminative features, supported by eigenvalue spectra analyses. Supervised classifiers including SVM, logistic regression, random forests, and naive Bayes achieve promising accuracies above 75%, enabling support for individualized, remote diagnostic assessment and informing future multi-modal sensing work.","Intelligence-Based Medicine 11 (2025) 100217  \nContents lists available at ScienceDirect  \nIntelligence-Based Medicine  \njournal [homepage:](homepage: www.sciencedirect.com/journal/intelligence-based-medicine)[ www.sciencedirect.com/journal/intelligence-based-medicine](homepage: www.sciencedirect.com/journal/intelligence-based-medicine)  \n| Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases\u003Cbr>*\u003Cbr>Timothy Albiges , Zoheir Sabeur , Banafshe Arbab-Zavar \u003Cbr>Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Artificial intelligence Machine learning\u003Cbr>COPD\u003Cbr>Eigenvalue spectral densities Eigenspaces\u003Cbr>Signal analysis\u003Cbr>Transfer learning Projection |  | Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease’s ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life.\u003Cbr>Auscultation lung sound analysis has emerged as a valuable, non-invasive, and cost-effective remote diagnostic tool of the future for respiratory conditions such as COPD. This research paper introduces a novel machine learning-based approach for classifying multiple COPD severities through the analysis of lung sound data streams. Leveraging two open datasets with diverse acoustic characteristics and clinical manifestations, the research study involves the transformation and decomposition of lung sound data matrices into their eigenspace representation in order to capture key features for machine learning and detection. Early eigenvalue spectra analyses were also performed to discover their distinct manifestations under the multiple established COPD severities. This has led us into projecting our experimental data matrices into their eigenspace with the use of the manifested data features prior to the machine learning process. This was followed by various methods of machine classification of COPD severities successfully. Support Vector Classifiers, Logistic Regression, Random Forests and Naive Bayes Classifiers were deployed. Systematic classifier performance metrics were also adopted; they showed early promising classification accuracies beyond 75 % for distinguishing COPD severities.\u003Cbr>This research benchmark contributes to computer-aided medical diagnosis and supports the integration of auscultation lung sound analyses into COPD assessment protocols for individualised patient care and treatment. Future work involves the acquisition of larger volumes of lung sound data while also exploring multi-modal sensing of COPD patients for heterogeneous data fusion to advance COPD severity classification performance. |\n\n1. Introduction  \nIn the UK, Chronic Obstructive Pulmonary Disease (COPD) accounts for 1 in 8 patients attending Emergency Care due to the sudden worsening of the condition that is known as an \"Exacerbation Event\" (EE). The NHS manages 1.2 million patients with COPD and, unlike communicable diseases, it is expected to grow by 40 % by 2030 with annual costs exceeding £2.5 billion (NHS, 2023). There is also an expectation of nearly 2 million undiagnosed people who are living with COPD [1].  \nCOPD is a broad term for respiratory conditions that ","cbCaiqXJSPwPfkjg","https://ap.wps.com/l/cbCaiqXJSPwPfkjg","pdf",1929242,1,10,"English","en",105,"# Introduction\n## Background\n## Objective and study overview\n# Methods\n## Data sources and preprocessing\n## Eigenspace representation and feature extraction\n## Eigenvalue spectra analysis\n## Supervised classification models\n# Results\n## Classification performance metrics\n# Discussion\n## Implications for computer-aided diagnosis\n## Limitations and future work","[{\"question\":\"How does the study use lung sound data to classify COPD severities?\",\"answer\":\"It transforms lung sound data streams into matrix representations, decomposes them into eigenspace features, and uses the derived features as inputs to supervised classifiers for distinguishing COPD severities.\"},{\"question\":\"Which machine learning models are evaluated for COPD severity classification?\",\"answer\":\"Support Vector Classifiers, Logistic Regression, Random Forests, and Naive Bayes Classifiers are deployed, followed by systematic performance metric evaluation.\"},{\"question\":\"What performance level do the classifiers reach in the study?\",\"answer\":\"The adopted metrics show promising classification accuracies beyond 75% for distinguishing COPD severities based on the analyzed audio-derived features.\"}]","Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases - 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