Entrepreneurial Intentions Prediction of Dental Practitioners Using Spiking Neural Networks and Intelligent Survey Design

With the increasing demand for dental healthcare and the rapid growth of private dental clinics in Guangdong (South China), understanding the entrepreneurial intentions of dental practitioners has become a topic of significant interest. Questionnaire-based analysis is a practical approach for exploring the underlying relationships between entrepreneurial intentions and various influencing factors. However, two major challenges remain: designing meaningful survey questions and analyzing the inherently non-linear, symbolic nature of questionnaire data.To address these challenges, we propose a novel computational framework based on Spiking Neural Networks (SNNs) a brain-inspired model well-suited for sparse, binary-coded, and event-driven data such as survey responses. The proposed SNN-based Questionnaire Analysis Framework includes two key components: feature importance analysis for optimizing questionnaire design, and an SNN-based classifier for predicting dental practitioners’ entrepreneurial intentions. By leveraging the SNNs ability to model complex temporal and nonlinear interactions with high energy efficiency, our framework demonstrates superior performance in both interpretability and classification accuracy. Extensive experiments on real-world questionnaire data validate the effectiveness and robustness of the proposed method.