Predictive Modeling of Cancer Information Overload and Screening Attitudes: A Cross-Sectional Study.
Journal:
Seminars in oncology nursing
Published Date:
Dec 12, 2025
Abstract
OBJECTIVE: To explore the association between cancer information overload and attitudes toward cancer screening among internal medicine patients. AIMS: To identify predictors of screening attitudes using statistical and machine learning models. METHODS: A cross-sectional study was conducted with 410 internal medicine outpatients. Data were collected using the Cancer Information Overload Scale and the Attitude Scale for Cancer Screening. Analyses included t-tests, ANOVA, and regression. Seven machine learning models (KNN, SVM, ANN, RF, XGBoost, CART, Elastic Net) were compared using 10-fold cross-validation (R², RMSE, MAE). Statistical significance was set at P < .05. RESULTS: Participants' mean age was 38.25 ± 12.46 years; 69.8% were female. Mean information overload and attitude scores were 17.14 ± 4.91 and 100.13 ± 13.58, respectively. Regression analysis showed a significant negative association between information overload and screening attitudes (β = -0.321, P < .001; R² = 0.103; 95% CI [-1.144, -0.634]). Among machine learning models, Elastic Net Regression (α = 0.2, λ = 1) achieved the best performance (RMSE = 12.6, MAE = 10.8), confirming cancer information overload as the strongest predictor. CONCLUSION: Cancer information overload is inversely associated with screening attitudes. Machine learning models enhance interpretability, emphasizing the importance of managing information burden to improve cancer screening engagement.
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