ScholarWorks@성균관대학교

초록

Purpose This study investigated the disability acceptance among individuals with disabilities by identifying the key factors influencing the degree of acceptance and analyzing these factors using machine learning techniques. Method Using data from the 2022 5th Disability Life Panel Survey (4, 520 individuals) conducted by the Korea Disability Development Institute, this study compared machine learning models (e.g., Random Forest, Bagging, XGBoost, KNN, SVM, AdaBoost, Naïve Bayes) to predict acceptance levels. The performance metrics included accuracy, precision, recall, F1 score, and RMSE. The study also explored the main variables influencing disability acceptance, focusing on the results derived through bagging techniques. Results Self-esteem and family health were the most critical factors influencing acceptance of disability, ranking first, second, and fourth among the 29 predictors. Other significant factors included depression, emotional support, relationships with family and friends, and social participation. Conclusion Based on the findings, conclusions often come at last, discussion, suggestions for further research are presented. © 2025 Seorim. All rights reserved.

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