Predictive Capabilities of the Logistic Regression Method for Personalized Screening of Attitudes towards Vaccination

Relevance: Vaccination is an effective method of prevention for many infectious and non-infectious diseases. However, distrust in immunization has become a global public health challenge. Personalized tools for assessing attitudes toward vaccination are necessary to improve population adherence to vaccination programs.

Aims. To develop a binary logistic regression model for personalized assessment of an individual's attitude toward immunization.

Materials and methods. The study utilized results from an anonymous survey of 3,082 respondents (2,087 medical students and 995 non-medical students). The data were split into training (80 %) and testing (20 %) datasets. A binary logistic regression model was constructed based on responses to seven survey questions. Model evaluation was performed using ROC analysis, sensitivity, specificity, accuracy, and the F1-score.

Results. The developed model is statistically significant (p < 0.001) and explains 50.1 % of the variance in attitudes toward vaccination. The area under the ROC curve (AUC) was 0.888, indicating high discriminative ability. On the test dataset, the model demonstrated a sensitivity of 82.3 %, specificity of 86.7 %, and accuracy of 83.3 %.

Conclusion. The developed binary classifier exhibits high predictive capability for personalized assessment of an individual's attitude toward immunization. Its application can enable healthcare professionals to effectively identify individuals with uncertain or negative attitudes toward vaccination and conduct targeted educational interventions to improve vaccination adherence. To enhance the model's universality and accuracy, further training incorporating representatives of various social groups is required.

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