Predicting the probability of finding missing older adults based on machine learning

Abstract

Person missingness is an enigmatic and frequent phenomenon that can bring about negative consequences for the missing person, their family, and society in general. Age-related cognitive changes and a higher vulnerability to dementia can increase the propensity of older adults to go missing. Thus, it is necessary to better understand the phenomenon of missingness in older adults. The present study sought to identify individual and environmental factors that might predict whether an older adult reported missing will be found. Supervised machine learning models were used based on the missing person cases open data of Colombia between 1930 and June 2021 (n = 7855). Classification algorithms were trained to predict whether an older adult who went missing would eventually be found. The classification models with the best performance in the test data were those based on gradient boosting. Particularly, the Gradient Boosting Classifier and the Light Gradient Boosting Machine algorithms showed, respectively, 10% and 9% greater area under the curve (AUC) of the receiver operating characteristic (ROC) curve than a data-driven, reference model based on the mean of the reported time elapsed since the missingness observed in the training data. The features with the greatest contribution to the classification were the time since the missingness, the place where it occurred, and the age and sex of the missing person. The present results shed light on the societal phenomenon of person missingness while setting the ground for the application of machine learning models in cases of missing older persons.