Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques

Alzheimer's disease (AD) is considered as progressing brain disease, which can be slowed down with the early detection and proper treatment by identifying the early symptoms. Language change serves as an early sign that a patient's cognitive functions have been impacted, potentially leading to early detection. The effects of language changes are being studied thoroughly in the English language to analyze the linguistic patterns in AD patients using Natural Language Processing (NLP). However, it has not been much explored in local languages and low-resourced languages like Nepali. In this paper, we have created a novel dataset on low resources language, i.e., Nepali, consisting of transcripts of the AD patients and control normal subjects. We have also presented baselines by applying various machine learning (ML) and deep learning (DL) algorithms on a novel dataset for the early detection of AD. The proposed work incorporates the speech decline of AD patients in order to classify them as control subjects or AD patients. This study makes an effective conclusion that the difficulty in processing information of AD patients reflects in their speech narratives of patients while describing a picture. The dataset is made publicly available.