Support Vector Machine Classification of Cyberchondria Severity Based on Health Anxiety, Intolerance of Uncertainty, and Online Reassurance Seeking
Keywords:
Cyberchondria, Support Vector Machine, Health Anxiety, Intolerance of Uncertainty, Online Reassurance Seeking, ClassificationAbstract
Objective: The present study aimed to classify cyberchondria severity based on health anxiety, intolerance of uncertainty, and online reassurance seeking using a Support Vector Machine model among adults living in Canada.
Methods and Materials: This applied, quantitative, cross-sectional study was conducted using a predictive machine-learning classification design. The statistical population included adults living in Canada who used the Internet to search for health-related information. A total of 426 participants were selected through online convenience sampling. Data were collected using the Cyberchondria Severity Scale-12, the Short Health Anxiety Inventory, the Intolerance of Uncertainty Scale-12, an online reassurance-seeking measure, and a demographic information form. Cyberchondria severity was classified into low, moderate, and high groups using percentile-based categorization. The dataset was divided into training and testing subsets using stratified sampling, with 70% of the data used for model training and 30% reserved for independent testing. Support Vector Machine models with different kernels were trained and compared through cross-validation, and the final model was evaluated using accuracy, precision, recall, F1-score, area under the curve, and confusion matrix analysis.
Findings: The radial basis function Support Vector Machine showed the best cross-validated performance and was selected as the final model. In the independent test set, the final model achieved an overall accuracy of 0.84, macro F1-score of 0.84, weighted F1-score of 0.84, and macro AUC of 0.92. Class-level results showed strong performance for low cyberchondria severity and high cyberchondria severity, with F1-scores of 0.87 and 0.89, respectively. The moderate severity group showed lower but acceptable classification performance, with an F1-score of 0.76. The confusion matrix indicated that classification errors occurred mainly between adjacent severity categories, with no direct confusion between low and high severity groups. Permutation-based importance analysis indicated that online reassurance seeking was the strongest predictor, followed by health anxiety and intolerance of uncertainty.
Conclusion: The findings showed that cyberchondria severity can be accurately classified using a Support Vector Machine model based on health anxiety, intolerance of uncertainty, and online reassurance seeking. The results highlight the importance of anxiety-related cognition, difficulty tolerating health uncertainty, and repeated digital reassurance seeking in identifying individuals at greater risk for severe cyberchondria.
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References
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