Uncovering Disappearance Dynamics in Peru: A Two-Stage Multimodal Framework for Behavioral Segmentation and Voluntariness Prediction in Missing-Person Reports

Aybar-Flores A.; Maehara R.; Benites L.; Muñoz M.; Salinas J.C. · Lecture Notes in Networks and Systems (2026) · DOI: 10.1007/978-3-032-20752-4_1

Resumen

Disappearances in Peru remain underexamined: over 50% of reported individuals are never located, and institutional gaps persist. We present a two-stage multimodal framework using RENIPED structured records, narrative descriptions, and facial images. First, Gaussian Mixture Models uncover five behavioral profiles; clustering performs best with structured attributes, revealing patterns tied to reporting delays, ethnicity, and regional origin. Second, a multimodal regressor—using only structured and image data—estimates the likelihood of voluntary vs. involuntary disappearances, achieving a mean absolute percentage error of 1.12% without relying on text. Findings show that machine learning can surface latent patterns and support investigations in data-limited settings. Outcomes are shaped by gender, geography, and reporting lags, highlighting the need for more responsive, context-sensitive interventions. Code and implementation: https://github.com/yoce3/MissingPersonAnalisys. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Instituciones: Universidad del Pacífico