A penalized regression and machine learning approach for quality-of-life prediction in psoriasis patients

Psoriasis is a chronic inflammatory skin disease that significantly affects patients’ quality of life (QoL), as measured by the Dermatology Life Quality Index (DLQI). This study employs penalized regression and machine learning (ML) techniques to develop predictive models for DLQI in psoriasis patients. Using a dataset of 149 Thai patients, 16 models including multiple linear regression (MLR), five penalized regression models, five Random Forest (RF) models, and five Support Vector Regression (SVR) models were trained. Feature selection was performed using ridge, LASSO, adaptive LASSO, elastic net, and adaptive elastic net to optimize predictive accuracy and interpretability. Results indicate that RF-L1L2, a Random Forest model trained on elastic net-selected features, achieved the best performance with the lowest Root Mean Square Error (RMSE) of 5.6344, and lowest Mean Absolute Pencentage Error (MAPE) of 35.5404, outperforming traditional regression models. Bland–Altman analysis further confirmed the superiority of RF models in reducing systematic bias and improving predictive agreement. However, our findings should be interpreted with caution due to the limitations of small-sample size modeling. Key features included four psychological stress factors, age, Psoriasis Area and Severity Index (PASI), comorbidities and gender, reinforcing the interplay between physical and mental health. SHapley Additive exPlanations (SHAP) was employed in model explainability. Integrating ML models into clinical decision-making, can enhance patient stratification and personalized treatment strategies, with potential applications in AI-driven healthcare solutions.