An Explainable AI Framework for Vibroarthrographic Knee Joint Disorder Detection Using Entropy Based Feature Engineering

An Explainable AI Framework for Vibroarthrographic Knee Joint Disorder Detection Using Entropy Based Feature Engineering | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Explainable AI Framework for Vibroarthrographic Knee Joint Disorder Detection Using Entropy Based Feature Engineering Monishka Mittal, Jaiditya Abhineet Kapoor, Ojaswi Kumar, Divya Arora, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8694157/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this work, a computer-aided diagnosis (CAD) system is proposed to classify knee joint conditions into healthy and non-healthy categories using vibroarthrographic (VAG) signals. To effectively analyze the non-stationary nature of VAG signals, the Tunable Q-factor Wavelet Transform (TQWT) is employed to decompose the signals into multiple sub-band components. From these sub-bands, eight entropy-based features are extracted, yielding a total of 400 features. Feature selection techniques are then applied to identify the most relevant descriptors, yielding 45 optimal features. Six machine learning classifiers, along with an ensemble learning strategy, are utilized for classification. The ensemble-based framework achieves 90.1% classification accuracy and an AUC of 0.91 under leave-one-subject-out validation. Furthermore, an explainable artificial intelligence (XAI) approach based on Shapley Additive exPlanations (SHAP) is incorporated to interpret the model predictions by quantifying the contribution of individual entropy features. This explainability enhances the transparency and reliability of the proposed system, supporting its potential applicability in clinical decision-making. ensemble learning explainable artificial intelligence entropy feature selection vibroarthrography Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8694157","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581744498,"identity":"2cd2cab8-b497-4d50-a782-7b1ace15b130","order_by":0,"name":"Monishka Mittal","email":"","orcid":"","institution":"Thapar Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Monishka","middleName":"","lastName":"Mittal","suffix":""},{"id":581744499,"identity":"165d7e51-db2d-4d70-82fd-3a4dd754d640","order_by":1,"name":"Jaiditya Abhineet Kapoor","email":"","orcid":"","institution":"Thapar Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jaiditya","middleName":"Abhineet","lastName":"Kapoor","suffix":""},{"id":581744500,"identity":"ce63fb1f-a678-44db-8a02-dd043f1b719f","order_by":2,"name":"Ojaswi Kumar","email":"","orcid":"","institution":"Thapar Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ojaswi","middleName":"","lastName":"Kumar","suffix":""},{"id":581744501,"identity":"696fe4e7-2d86-433c-a347-5e6af914c8a3","order_by":3,"name":"Divya Arora","email":"","orcid":"","institution":"Thapar Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"","lastName":"Arora","suffix":""},{"id":581744502,"identity":"774a13e4-71ba-40f5-97f9-48081ff08bfb","order_by":4,"name":"Anjula Mehto","email":"","orcid":"","institution":"Thapar Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Anjula","middleName":"","lastName":"Mehto","suffix":""},{"id":581744503,"identity":"bdd20975-03f7-4eff-95ac-07a8e4c7874f","order_by":5,"name":"Saif Nalband","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYFACxoYPQFLGAEhIfGA4ABVlw6ulcQaQ5AFpkZyRQJQWBka4FmkeYrTItx9ubGDcYcdjzn724G3bH3fkgYwHDB/KDuPUYnAmEajlTDKPZU9esnVOwjPDnT3pBowzzuHRwpDY/oCxjZnH4ECOmXROwmHGDQfSGJh523Brke9/CLSlrZ7H4PwbM2mLhMP2G84/Y2D+i0cLww2Qw9oO8xjcANrCkHA4ccMNoC2MeLQY3ADakth2nMdyxhtjy560w8k7ZzxjONhzLh2Pw9IfNnxsq5Yz588xvPHD5rDtdv40xgc/yqxxOwwEElDsBeID+NVjOJU05aNgFIyCUTACAADOnVsEmgx41QAAAABJRU5ErkJggg==","orcid":"","institution":"Thapar Institute of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Saif","middleName":"","lastName":"Nalband","suffix":""},{"id":581744504,"identity":"362f0867-c46a-4a1b-ba49-4a19de5a32be","order_by":6,"name":"Femi Robert","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Femi","middleName":"","lastName":"Robert","suffix":""},{"id":581744505,"identity":"7cb2a14d-5cb6-4c9e-a97a-7fc8b8ae5369","order_by":7,"name":"A. 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To effectively analyze the non-stationary nature of VAG signals, the Tunable Q-factor Wavelet Transform (TQWT) is employed to decompose the signals into multiple sub-band components. From these sub-bands, eight entropy-based features are extracted, yielding a total of 400 features. Feature selection techniques are then applied to identify the most relevant descriptors, yielding 45 optimal features. Six machine learning classifiers, along with an ensemble learning strategy, are utilized for classification. The ensemble-based framework achieves 90.1% classification accuracy and an AUC of 0.91 under leave-one-subject-out validation. Furthermore, an explainable artificial intelligence (XAI) approach based on Shapley Additive exPlanations (SHAP) is incorporated to interpret the model predictions by quantifying the contribution of individual entropy features. 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