An ensemble approach for the diagnosis of cognitive decline with missing data

Abstract

This work applies new techniques of automatic learning to diagnose neuro decline processes usually related to aging. Early detection of cognitive decline (CD) is an advisable practice under multiple perspectives. A study of neuropsychological tests from 267 consultations on 30 patients by the Alzheimer's Patient Association of Gran Canaria is carried out. We designed neural computational CD diagnosis systems, using a multi-net and ensemble structure that is applied to the treatment of missing data present in consultations. The results show significant improvements over simple classifiers. These systems would allow applying policies of early detection of dementias in primary care centers where specialized professionals are not present.