Pattern Identification Using Fuzzy Cluster Analysis and Latent Class Analysis: A Case Study in Perú

The Demographic and Family Health Survey (ENDES) conducted by the National Institute of Statistics and Informatics (INEI) in Peru provides data on fertility and health. The ENDES 2020 report, based on 35,847 surveyed households, undergoes descriptive statistical analysis with the aim of identifying patterns to enhance social conditions. Techniques such as Fuzzy C-Means and Latent Classes, previously applied in various contexts, are employed. Correlation analysis using the R polycor package highlights significant relationships, leading to the exclusion of certain numeric variables in fuzzy clustering due to strong correlations. Random sampling is applied to address the data volume. Three clusters are determined through kmeans clustering, silhouette, Elbow, and Clara methods, assessing their fuzziness with the Dunn's Fuzziness Coefficient. Pattern identification reveals significant differences in family relationships, gender, education, and health insurance among the clusters. The widespread lack of health insurance, particularly ESSALUD/IPSS, stands out as a common issue. Fuzzy clustering and latent class analysis techniques provide groupings with variations in sizes and compositions. Received: 20 March 2024 / Accepted: 28 June 2024 / Published: 02 July 2024