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Predicting Family Intimacy in Cancer Patients Using Interpretable Machine Learning: Emphasizing Resilience and Self‐Esteem
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Predicting Family Intimacy in Cancer Patients Using Interpretable Machine Learning: Emphasizing Resilience and Self‐Esteem
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Journal Article
Predicting Family Intimacy in Cancer Patients Using Interpretable Machine Learning: Emphasizing Resilience and Self‐Esteem
2025
Overview
This study aimed to construct interpretable machine learning models to predict family intimacy in cancer patients and identify the most influential predictors through SHAP-based analysis. A total of 259 cancer patients were surveyed. The data cleaning process involved handling missing values, normalizing continuous variables, and applying one-hot encoding to categorical variables. Statistically significant sociodemographic variables (age, marital status, education, and income) and psychosocial attributes (self-esteem and three resilience subdimensions: tenacity, strength, and optimism) were selected using LASSO regression. Four regression models--gradient boosting (GB), random forest (RF), XGBoost (XGB), and decision tree (DT)--were trained and evaluated using R[sup.2], mean-squared error (MSE), and mean absolute percentage error (MAPE).SHapley Additive exPlanations (SHAP) was used to interpret the GB model. The GB model achieved the best predictive performance (R[sup.2]=0.6985, MSE=0.2405), followed by XGB (R[sup.2]=0.6794), RF (R[sup.2]=0.6653), and DT (R[sup.2]=0.5912). SHAP analysis revealed that psychological variables--tenacity, strength, and self-esteem--were the most influential predictors, all exerting strong positive effects. Age group and education showed moderate impact, while income, gender, and marital status contributed minimally. Gradient boosting offers a robust and interpretable framework for predicting family intimacy in cancer patients. Positive psychological resources--especially resilience and self-esteem--outperform traditional demographics as a predictive foundation, highlighting their clinical significance in survivorship care planning.
