Support Vector Machine Classification of Low Quality of Life Among Breast Cancer Survivors Using Fatigue, Depression, and Social Support Indicators
Keywords:
Breast cancer survivors, quality of life, support vector machine, fatigue, depression, social support, machine learning, survivorshipAbstract
Objective: This study aimed to classify low quality of life among breast cancer survivors using a support vector machine model based on fatigue, depression, and perceived social support indicators.
Methods and Materials: This cross-sectional predictive study was conducted among 312 female breast cancer survivors receiving follow-up care in oncology clinics and survivorship units in Tehran, Iran. Quality of life was assessed using the EORTC QLQ-C30, and participants with global health status/quality-of-life scores below 50 were classified as having low quality of life. Fatigue was measured using the Functional Assessment of Chronic Illness Therapy–Fatigue scale, depression using the Beck Depression Inventory-II, and perceived social support using the Multidimensional Scale of Perceived Social Support. After data screening and standardization, the dataset was divided into stratified training and testing subsets using an 80:20 ratio. A support vector machine classifier with a radial basis function kernel was developed, and model hyperparameters were optimized through grid search and cross-validation.
Findings: Participants with low quality of life had significantly higher depression scores, greater fatigue burden, and lower total perceived social support compared with survivors without low quality of life. Significant differences were also observed in family support, friend support, and significant-other support. The support vector machine model achieved strong classification performance, with cross-validation accuracy of 0.85 and independent test accuracy of 0.84. In the independent test set, sensitivity was 0.79, specificity was 0.87, precision was 0.79, F1-score was 0.79, balanced accuracy was 0.83, and the area under the receiver operating characteristic curve was 0.90. Permutation-based importance analysis showed that fatigue was the most influential predictor, followed by depression, total perceived social support, and family support.
Conclusion: The support vector machine model classified low quality of life among breast cancer survivors with high accuracy, indicating that fatigue, depression, and perceived social support are clinically meaningful indicators for identifying survivors at risk of poor quality of life.
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