Clustering Analysis for Extracting Moroccan Health Provinces Typology According to Breast and Cervical Cancer Early Screening

Cancer remains a major global concern, and its screening is a complex public health intervention. In Morocco, breast and cervical cancers are the most frequent malignancies among women, accounting for about half of all diagnosed cases. However, screening participation and coverage still vary across provinces. This study proposes a provincial typology of early screening performance using collected indicators for breast and cervical cancer. Before clustering, we applied several dimensionality reduction methods to improve cluster separability. We adopt a comparative framework that evaluates combinations of DR techniques (PCA, ICA, kernel PCA, t-SNE, and LLE) and clustering algorithms (ACH, K-Means, and GMM) to identify the optimal model with the help of internal validation measures. Kernel PCA with K-Means presents the most optimal model, producing the most coherent province clustering from all tested combinations (DR & algorithm clustering). It demonstrates the best overall separation and compactness according to the evaluation metrics. Three clusters were obtained describing a gradient of early screening system performance: the first group of provinces shows higher screening coverage and stronger diagnostic and referral capacity, the second group demonstrates intermediate performance and differentiated service delivery, and the third group of provinces with low coverage and restrictive access reflects geographic remoteness and service constraints. These results emphasize marked spatial disparity in preventive service performance. They demonstrate how unsupervised learning can support territorial health analysis. The resultant typology can inform targeted action: maintaining and sustaining quality in high-performing provinces, strengthening operations in intermediate-performing provinces, and giving priority to catch-up interventions in low-performing areas.

@article{Chakkouch2026,
  title     = {Clustering Analysis for Extracting Moroccan Health Provinces Typology According to Breast and Cervical Cancer Early Screening},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Meryem Chakkouch and Merouane Ertel and Aziz Mengad and Said Amali and Majda Frindy},
  doi       = {10.14569/IJACSA.2026.0170379},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170379}
}