Early-stage detection of eye diseases on microblogs: glaucoma recognition

Glaucoma is the most popular optic neuropathy that causes blindness in people without warning signs. The early detection of glaucoma is crucial for an early treatment that could be useful to delay vision loss. However, since vision loss caused by glaucoma cannot be recovered, this study proposes an early detection mechanism for glaucoma using social media posts. Glaucoma-related tweets were collected using the Twitter streaming application programming interface (API). A hierarchical clustering algorithm was applied to group the tweets that share similar features together. In each cluster, the co-occurrence analysis was applied using the VOSViewer technique to map specific disease-related terminologies. Users’ emotions (e.g., anger, fear, sadness, and joy) and their polarity (positive, neutral, and negative) were extracted using NRC (Affect Intensity Lexicon) and SentiStrength techniques. The detection of glaucoma was achieved by using multinomial logistic regression (Logistic). The classification results showed that the Logistic classifier was able to predict glaucoma tweets with 98.73% accuracy. Our findings revealed that negative, fear, and sadness sentiments can be useful in detecting glaucoma. This study provides an effective mechanism to detect glaucoma disease from Twitter messages

A lexicon-based method for detecting eye diseases on microblogs

This paper explored the feasibility of detecting eye diseases on microblogs. A lexicon-based approach was developed to provide an early recognition of common eye disease from social media platforms. The data were obtained using Twitter free streaming Application Programming Interface (API). A cluster analysis was applied to extract instances that share similar characteristics. We extracted three types of emotions (positive, negative, and neutral) from users’ messages (tweets) using SentiStrength. A time-series method was used to determine the applicability of predicting emotional changes over a period of seven months. The relevant disease symptoms were extracted using Apriori algorithm with prediction accuracy of 98.89%. This study offers a timely and effective method that can be implemented to help healthcare decision makers and researchers reduce the spread of eye diseases in a population specific manner

Emotional Intelligence and Individual Visual Preferences: A Predictive Machine Learning Approach

Differences in individuals’ psychological and cognitive characteristics have been always found to play a significant role in influencing our behavior and preferences. While a number of studies have identified the impact of these characteristics on individuals’ visual design preferences, understanding how emotional intelligence (EI) would influence this process is yet to be explored. This study investigated the link between individuals’ EI dimensions (eg, emotionality, self-control, sociability, and well-being) and their eye movement behavior in an attempt to build a prediction model for visual design preferences. A total of 136 participants took part in this study. The feature selection and prediction of EI and eye movement data were performed using the genetic search method in conjunction with the bagging method. The results showed that participants high in self-control and emotionality exhibited different eye movement behaviors when performing five visual selection tasks. The prediction results (93.87% accuracy) revealed that specific eye parameters can predict the link between certain EI dimensions and preferences for visual design. This study adds new insights into human–computer interaction, EI, and rational choice theories. The findings also encourage researchers and designers to consider EI in the development of intelligent and adaptive systems