Developing Topics.

Abstract

The growth of social media and the continuous improvement of machine-learning algorithms suggest that social media-based screening methods for mental diseases will become increasingly feasible with high accuracy in the next few years. Additionally, artificial intelligence, particularly predictive machine learning (ML) Models, has been established as one of the more powerful approaches to building reliable models that might be useful as an early predictor for Mental disorders. Specifically, one of the current challenges in brain disorders is identifying patients with mild cognitive impairment (MCI) that might be converted to Alzheimer's (AD) or other types of dementia. Cognitive decline in older adults is associated with decreased social interaction and changes in other features. We aim to use vast quantities of historical Facebook data from participants who conducted the MoCA test as a cognitive assessment test to examine if there are changes in their language mistakes and in a range of psychological features derived from natural language processing use and social engagement over time as a sign of MCI. To develop a Machine learning model to predict MCI using (n = 225), we should first consider how we can formulate Language mistakes and social interactions. Then, we aim to perform a longitudinal and cross-sectional examination of the data to examine the changes in a range of features extracted from Facebook subjects' data over time, using NLP algorithms, LIWC 2022, Go-emotion dataset, and supervised Classification ML tools, such as support vector machine model (SVM). We are collecting identified Facebook data, de-identifying it, translating the Arabic text into English, processing text augmentation and text preprocessing, defining language mistakes, social engagement over time features, and sentiments impeded in each syntax. Our classification model SVM shows significant prediction results, differentiating between patients (MCI& dementia) and normal subjects, using a sample of 225 subjects, resulting that the SVM prediction test score is 70%. Using our suggested pipeline, Our classification model SVM shows promising primary results in building a predictive model that has the ability for early detection of MCI.