Data Mining and Behavioural Analysis in Neurological Health Communication: An AI–Driven Neuroinformatics Perspective.

Abstract

Effective neurological health communication is vital for improving public understanding, early diagnosis, and behavioural adaptation toward brain-related disorders. However, the vast and unstructured nature of digital communication has created challenges in extracting meaningful behavioural and emotional patterns relevant to neuroscience. This study proposes a neuroinformatics-driven data mining framework that employs Natural Language Processing (NLP), machine learning, and sentiment analysis to explore how neurological health information is discussed and perceived online. Using a large corpus of social media and digital health discourse from 2020–2024, the study models the linguistic, affective, and topical dimensions of neurological communication. A hybrid computational pipeline integrating Latent Dirichlet Allocation (LDA) for topic modelling and Bidirectional Encoder Representations from Transformers (BERT) for contextual sentiment analysis was implemented to identify thematic clusters in discussions about neurological disorders such as Alzheimer’s, epilepsy, and stroke. The results show distinct communication patterns: emotional empathy dominates patient-centered discourse, while fear and misinformation drive spikes in public engagement. Temporal analysis reveals evolving attention cycles beginning with awareness, progressing through anxiety, and stabilizing into informed discussion mirroring neuro-behavioural adaptation. This study contributes a data mining model for neuro – behavioural communication analytics, providing new insights into how public perceptions and emotional responses toward neurological health evolve. By aligning computational intelligence with cognitive communication theories, the framework bridges neuroinformatics and behavioural neuroscience, offering a foundation for designing data – driven neurological education and communication interventions.