<![CDATA[Postpartum depression and anxiety often go undetected, and breastfeeding experience is among the factors most strongly linked to both. However, previous research has largely been supervised, dependent on biomarkers, and rarely captures the heterogeneity of risk profiles among mothers with similar symptom levels. This study proposes Clinically Weighted Feature Fusion (CWFF), a clustering framework that weights clinical composite features based on their out-of-fold association with EPDS and GAD-7 scores, to map postpartum depression and anxiety risk subgroups among 2,010 breastfeeding mothers in the United Kingdom. As a foundation, 22 composite features representing five clinical domains were compared with 30 raw variables using K-Means (k = 4). The engineered approach with equal weighting (equal-weight) showed better clustering quality, marked by a 28.2% increase in the Silhouette Score, a 21.8% decrease in the Davies–Bouldin Index, and a 26.8% increase in the Calinski–Harabasz Index; all significant based on bootstrap testing (p < 0.0001). Ablation analysis of leave-one-domain-out and leave-one-interaction-term-out (Friedman χ² = 794.15; p < 0.001) shows that this improvement mainly comes from Interaction Features and Mental Health History, not from all domains uniformly. This advantage is also only consistent for K-Means and Agglomerative Clustering, not for Gaussian Mixture Model (GMM) or HDBSCAN. Then, applying CWFF, the results show that CWFF raises Silhouette from 0.1537 to 0.2418 while also raising clinical validity (η²_EPDS from 0.071 to 0.096; η²_GAD-7 from 0.090 to 0.123), with all improvements significant (bootstrap 95% CI does not include zero). These findings indicate that clinically informed weighting improves risk-subgroup identification compared to equal-weight, potentially supporting low-cost postpartum screening in primary healthcare settings.]]>
