Graph clustering and prediction models for DISC-based personality and competency analysis

The DISC framework is widely used to describe behavioral styles in organizations, but it is often applied through static and qualitative interpretation. This study combines graph-based clustering with supervised learning to analyze DISC-style profiles, competencies, and stress outcomes. Using a real-world dataset of 195 employees described by 97 heterogeneous attributes, we construct a weighted similarity graph by fusing (i) cosine similarity of 17 ordinal competency levels, (ii) exact-match similarity of organizational context variables, and (iii) Jaccard similarity of trait-like descriptors. Modularity-based community detection is applied to reveal latent behavioral groups. Random Forest models are then used to predict stress-related outcomes. For 4-class stress prediction (Low, Medium, High, High (Work-related)), stratified 5-fold cross-validation yields an average accuracy of 52.82%. This is above the uniform random baseline (25%) but below the majority-class baseline ($$\approx 58.97\%$$), indicating moderate predictive signal. Variable-importance analysis suggests that sales-related competency levels contribute strongly to stress differentiation in this cohort. A separate experiment on competency-group prediction reaches near-perfect accuracy, but this is expected because the target is derived from the same competency descriptors used as inputs and therefore reflects information leakage rather than generalizable prediction. Overall, the study shows how DISC assessments can be extended into graph-based and predictive organizational analytics, while also clarifying the limits of what can be inferred from cross-sectional survey attributes.