Artificial Intelligence in Questionnaire-Based Research: Quality of Life Classification Across Different Population Groups

This interdisciplinary study presents a novel questionnaire analysis methodology using Artificial Intelligence (AI) and Machine Learning (ML). The framework is broadly applicable to all areas of research using questionnaire data analysis, including health sciences and physical education. Our predictive modeling was based on the XG-Boost algorithm, which classified individuals into three distinct groups—employees and two cohorts of retirees—based on their demographic profiles and responses to the WHOQOL-BREF survey. In order to ensure the credibility and reliability of the predictions, the model building process used the implementation of cross-validation. This procedure produced a model with a resultant accuracy of 0.8038 (95% confidence interval: 0.7551–0.8908). To go beyond conventional performance metrics, we implemented the SHapley Additive exPlanations (SHAP) method, providing a transparent and detailed interpretation of the model’s decision-making process. This explainable AI analysis clarifies both the magnitude and direction of the impact of key factors such as age and various predictors of quality of life, providing detailed, data-driven insights into what differentiates groups.