Advancing Personal Well-Being Analysis through the Integration of Instance-Based Learning with Interpretable AI Techniques

Advancing Personal Well-Being Analysis through the Integration of Instance-Based Learning with Interpretable AI Techniques | 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 Article Advancing Personal Well-Being Analysis through the Integration of Instance-Based Learning with Interpretable AI Techniques Md Sarwar Kamal, Nasrin Sultana, Michael Bewong, Ryan H.L. Ip, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4015335/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 Understanding factors that affect Personal Well-Being Index (PWI) is crucial for developing strategies that enhance quality of life at both individual and community levels. This research introduces a novel approach to PWI analysis by integrating Instance-Based Learning (IBL) with interpretable Artificial Intelligence (AI) methods. Utilizing IBL, the study creates tailored matrices for individual instances, enabling a dynamic and specific analysis of well-being factors. It leverages advanced AI techniques, notably the graph-based clustering and Locally Weighted Scatterplot Smoothing (LOWESS), to provide transparent and comprehensible insights. Key innovations include the use of sophisticated feature selection techniques like Random Forest (RF), and Permutation Importance (PI) to identify critical PWI indicators. The research also employs LOWESS for state-of-the-art prediction modeling, providing adaptable forecasts for future trends in PWI indicators. The application of graph-based clustering offers a comprehensive analysis of the current state of PWI, generating detailed scenarios and enhancing the understanding of well-being. This integrative methodology positions the study at the forefront of PWI research, offering new pathways for enhancing well-being in diverse settings. Earth and environmental sciences/Climate sciences Physical sciences/Mathematics and computing Instance-based learning Graph-based clustering Permutation importance Locally weighted scatter plot smoothing Interpretable AI Personal well-being index 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-4015335","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283097217,"identity":"8805e52c-9a7b-4c56-9562-54947ba5803e","order_by":0,"name":"Md Sarwar Kamal","email":"","orcid":"","institution":"Charles Sturt University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Sarwar","lastName":"Kamal","suffix":""},{"id":283097220,"identity":"808b2f35-c317-4615-830d-5fc9cf8c2198","order_by":1,"name":"Nasrin Sultana","email":"","orcid":"","institution":"Charles Sturt University","correspondingAuthor":false,"prefix":"","firstName":"Nasrin","middleName":"","lastName":"Sultana","suffix":""},{"id":283097222,"identity":"17e50846-de22-4bf8-be13-41343ca388bf","order_by":2,"name":"Michael Bewong","email":"","orcid":"","institution":"Charles Sturt University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Bewong","suffix":""},{"id":283097224,"identity":"894321b4-f300-40c1-b5a2-1e3b37a08569","order_by":3,"name":"Ryan H.L. 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