Application of Machine Learning Techniques to Examine Social Service Needs among Hispanic Family Caregivers of Persons with Dementia

Abstract

We applied machine learning algorithms to examine the relationship between demographics and outcomes of the social work services used by Hispanic family caregivers of persons with dementia recruited for a clinical trial in New York City. The social work service needs were largely concentrated on instrumental support to gain access to the healthcare system rather than other concrete services (e.g., housing or food programs) or to address psychological needs among the caregivers with relatively higher income. A finding from the machine learning approach was that among those who receive medical-related social work services, frequent users (≥10 times) with high family friend support(>4) were more likely than frequent users without such support to have their issues resolved (Accuracy: 81.9%, AUC: 0.82, F-measure: 0.86 by J48). Even though half of the participants received social work services multiple times, the needs of the caregivers remained unmet unless they sought social work services frequently (more than ten times).