Parsimonious Sequential Feature Group Selection Methods for Predicting Treatment Outcome in Chronic Disorders

Parsimonious Sequential Feature Group Selection Methods for Predicting Treatment Outcome in Chronic Disorders | 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 Parsimonious Sequential Feature Group Selection Methods for Predicting Treatment Outcome in Chronic Disorders Hafez Kader, Uli Niemann, Rilana Cima, Dimitris Kikidis, Berthold Langguth, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4787524/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 the era of burgeoning high-dimensional data, particularly in fields likemedicine, where data collection is increasing, the need for effective feature selectionin machine learning is paramount. Feature selection helps mitigate challengessuch as noise and irrelevant information, leading to a deeper understanding ofresults and improved predictive capabilities. While traditional methods focus onindividual features, some data naturally groups into cohesive “feature groups”,such as those collected from sensors or assessment batteries. This paper aimsto rank and select these feature groups to maximize predictive performance. Itintroduces tailored feature selection methods targeting entire feature sets ratherthan individual features. By applying these methods to data from a randomizedclinical trial, which utilizes multiple assessment batteries for patient diagnosisand treatment, the study demonstrates improved predictions and insights intothe importance of specific assessment batteries. The results show that utilizingfeature group selection methods allows for the identification of crucial assessmentbatteries for prediction while excluding less significant ones, potentiallysaving costs, time, and computational resources. However, the study is limited to predefined feature groups. Feature Group Selection Pareto Front Sparsity Group Lasso 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-4787524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":340814917,"identity":"81221fa7-2989-4f22-87cc-470c3b79ebd3","order_by":0,"name":"Hafez Kader","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACAwbGB0AqgYHhAIQF4eDXwmwA0wJmkaaFTYIoLebshxkf/mBIk+c73vus4mNbbWIDe/IBvFose5KZDSQYcgxnnjludnNm2/HEBp5n+K0xOJB/TMKAoYJxw400tts8Z44lNkjkGODXcv4x+48Ehgr7DfefsRVDtOR/wK/lRjIb0Oc5iRtusLEx81TUgGzBqwOo5TGzZINBWvLMM2nMkjMqDhi38Twj5LBkxo8/KpJt+44fY/zwwaBOtp89+QF+ayAa4azDDGxEqEcBdaRqGAWjYBSMghEAADM/S1jQSAS1AAAAAElFTkSuQmCC","orcid":"","institution":"Otto-von-Guericke University Magdeburg","correspondingAuthor":true,"prefix":"","firstName":"Hafez","middleName":"","lastName":"Kader","suffix":""},{"id":340814918,"identity":"fe9a1a3e-1217-4dd0-9103-2ecad4cb1906","order_by":1,"name":"Uli Niemann","email":"","orcid":"","institution":"Otto-von-Guericke University Magdeburg","correspondingAuthor":false,"prefix":"","firstName":"Uli","middleName":"","lastName":"Niemann","suffix":""},{"id":340814919,"identity":"a82ac7f3-b7db-4992-8094-b0b73960d943","order_by":2,"name":"Rilana Cima","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"prefix":"","firstName":"Rilana","middleName":"","lastName":"Cima","suffix":""},{"id":340814920,"identity":"e0513616-fb91-44d6-a1de-3ba44f67918a","order_by":3,"name":"Dimitris Kikidis","email":"","orcid":"","institution":"National and Kapodistrian University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Dimitris","middleName":"","lastName":"Kikidis","suffix":""},{"id":340814921,"identity":"bfd059b1-75e1-49fa-ae98-7c5b7e35d2db","order_by":4,"name":"Berthold Langguth","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Berthold","middleName":"","lastName":"Langguth","suffix":""},{"id":340814922,"identity":"58f32b05-d19d-4b41-b5e0-71aaf26689f4","order_by":5,"name":"Jose Antonio Lopez-Escamez","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Antonio","lastName":"Lopez-Escamez","suffix":""},{"id":340814923,"identity":"4b69cd23-e871-4edb-b98a-ccaa69afa79a","order_by":6,"name":"Winfried Schlee","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Winfried","middleName":"","lastName":"Schlee","suffix":""},{"id":340814924,"identity":"7af061fb-91da-42fa-bd08-043f5c81ac7a","order_by":7,"name":"Stefan Schoisswohl","email":"","orcid":"","institution":"Bundeswehr University Munich","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Schoisswohl","suffix":""},{"id":340814925,"identity":"1fcfe416-8941-4df9-8cb5-d1ae6187f901","order_by":8,"name":"Birgit Mazurek","email":"","orcid":"","institution":"Charité - 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