Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vijaysankar, Kavya Title A name given to the resource Identifying the Determinants of Maladaptive Pain Perception and Response Patterns: Predictive Analysis With XGBoost and Random Forests Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Predictive Algorithms for Rehabilitation and Assistive Systems;pp.411-458 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/9798337301945.ch016" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/9798337301945.ch016</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012338004?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012338004?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vijaysankar K., Christ University, Bengaluru, India Description An account of the resource A complex interplay of psychological, biological, and social factors influences pain perception and response patterns. This study aims to identify the determinants of maladaptive pain perception and response patterns through predictive analysis using psychological variables such as resilience, neuroticism, extraversion, grit, and optimism. A quantitative approach was used, incorporating predictive modeling techniques (decision trees, XGBoost, and Random Forests) to assess pain cata strophizing and sensitivity among 305 Indians. The XGBoost classifier predicting clinically significant pain catastrophizing achieved an accuracy of 74%. Findings indicate that neuroticism is a key predictor of pain-related outcomes, with resil ience &amp; optimism serving as protective factors. The study highlights the potential for personalized interventions by utilizing machine learning models to optimize predictor levels for improved pain management. The results underscore the need for further research incorporating biological and environmental factors to develop holistic pain management strategies. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833730196-9; 979-833730194-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online