ItemBook Chapter

Identifying the Determinants of Maladaptive Pain Perception and Response Patterns: Predictive Analysis With XGBoost and Random Forests

Title
Identifying the Determinants of Maladaptive Pain Perception and Response Patterns: Predictive Analysis With XGBoost and Random Forests
Creator
Vijaysankar, Kavya
Description
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 & 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.
Source
Predictive Algorithms for Rehabilitation and Assistive Systems;pp.411-458
Date
01-01-2025
Publisher
IGI Global
Coverage
Vijaysankar K., Christ University, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833730196-9; 979-833730194-5;
Format
online
Language
English
Type
Book chapter

Citation

Vijaysankar, Kavya, “Identifying the Determinants of Maladaptive Pain Perception and Response Patterns: Predictive Analysis With XGBoost and Random Forests,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24528.

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