Using text and acoustic features to diagnose progressive aphasia and its subtypes
Kathleen Fraser, Frank Rudzicz, Elizabeth A. Rochon · 2013
This paper presents experiments in automatically diagnosing primary progressive aphasia (PPA) and two of its subtypes, semantic dementia (SD) and progressive nonfluent aphasia (PNFA), from the acoustics of recorded narratives and textual analysis of the resultant transcripts. In order to train each of three types of classifier (naive Bayes, support vector machine, random forest), a large set of 81 available features must be reduced in size. Two methods of feature selection are therefore compared – one based on statistical significance and the other based on minimum-redundancy-maximum-relevance. After classifier optimization, PPA (or absence thereof) is correctly diagnosed across 87.4% of conditions, and the two subtypes of PPA are correctly classified 75.6% of the time.