Outlier and Cutoff Adjustments in DLQI Prediction for Psoriasis Patients: A Cross-Sectional Study in Thailand
Keywords:
Cook’s distance, imbalance classification, logistic regression, non-communicable diseases, Youden’s indexAbstract
This study proposes a binary logistic regression (BLR) framework with outlier and cutoff adjustments for predicting the Dermatology Life Quality Index (DLQI) in stress-affected psoriasis patients. Data from 149 patients, with DLQI as a binary response and eight predictive features, were analyzed. Cooks distance and M-estimators were applied to address outliers, and alternative cutoff thresholds, including the proportional method and Youdens index, were employed to improve classification accuracy. Adopting Cook’s distance and Youden’s index, the modified BLR model outperformed others. Subsequently, age, comorbidity and stress were identified as significant features and their odd ratios (OR) are 0.9228, 3.8425 and 1.1448, respectively. Cooks distance demonstrated superior performance, yielding the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, the highest Cox and Snell R-squared ( ) and Nagelkerke’s R-squared (
), and improved classification metrics. Youdens index further optimized sensitivity and specificity. Models incorporating these adjustments exhibited robust predictive capabilities, enhancing DLQI classification. These findings highlight the importance of addressing outliers and selecting appropriate cutoff thresholds in BLR modeling, offering valuable insights for improving clinical assessments and treatment strategies for psoriasis patients affected by stress.
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