Evaluating progression of alzheimer’s disease by regression and classification methods in a narrative language test in Portuguese

Abstract

Automated discourse analysis aiming at the diagnosis of language impairing dementias already exist for the English language, but no such work had been done for Portuguese. Here, we describe the results of creating a unified environment, entitled Coh-Metrix-Dementia, based on a previous tool to analyze discourse, named Coh-Metrix-Port. After adding 25 new metrics for measuring syntactical complexity, idea density, and text cohesion through latent semantics, Coh-Metrix-Dementia extracts 73 features from narratives of normal aging (CTL), Alzheimer’s Disease (AD), and Mild Cognitive Impairment (MCI) patients. This paper presents initial experiments in automatically diagnosing CTL, AD, and MCI patients from a narrative language test based on sequenced pictures and textual analysis of the resulting transcriptions. In order to train regression and classification models, the large set of features in Coh-Metrix-Dementia must be reduced in size. Three feature selection methods are compared. In our experiments with classification, it was possible to separate CTL, AD, and MCI with 0.817 F1 score, and separate CTL and MCI with 0.900 F1 score. As for regression, the best results for MAE were 0.238 and 0.120 for scenarios with three and two classes, respectively.