Machine Learning-Based Prediction Model for Health-Related Quality of Life in Diabetic Patients

The increasing prevalence of diabetes mellitus (DM) and patients' lack of self-management awareness have led to a decline in health-related quality of life (HRQoL). Studies identifying potential risk factors for HRQoL in DM patients and presenting generalized models are relatively scarce. The study aimed to develop and evaluate a machine learning (ML)-based model to predict the HRQoL in adult diabetic patients and to examine the important factors affecting HRQoL. This study extracted factors from the Korea National Health and Nutrition Examination Survey database (2016-2020) based on situation-specific theory, and using data from 2,501 adult DM patients. We developed five ML-based HRQoL classifiers (logistic regression, naïve Bayes, random forest, support vector machine, and extreme gradient boosting (XGBoost) in DM patients. The developed ML model was evaluated using six evaluation metrics to determine the best model, and feature importance was computed based on Shapley additive explanations (SHAP) value. The XGBoost model showed the best performance, with an accuracy of 0.940, a recall of 0.943, a precision of 0.940, a specificity of 0.919, an F1-score of 0.942, and an area under the curve score of 0.984. Based on SHAP values, the top five significant predictors of HRQoL were self-rated health (1.898), employment (0.822), triglycerides (0.781), education level (0.618), and aspartate transaminase/alanine transaminase ratio (0.611). The findings confirmed that the ML-based prediction model achieved high accuracy (over 90%) in distinguishing stable and at-risk groups in terms of HRQoL among adult DM patients. The XGBoost model's superior performance supports its potential integration into routine diabetes care as a decision-support tool. Identifying high-risk individuals early can help healthcare providers implement targeted interventions to improve long-term health outcomes.