Cardiovascular Disease Risk Prediction via Social Media
This work addresses CVD risk prediction for public health monitoring by offering an alternative to traditional demographic methods, though it appears incremental as it applies existing NLP techniques to social media data.
Researchers tackled cardiovascular disease (CVD) risk prediction by analyzing emotions in tweets using sentiment analysis and machine learning, finding that this approach surpassed demographic data alone in identifying at-risk individuals, with performance evaluated using metrics like accuracy and F1 score.
Researchers use Twitter and sentiment analysis to predict Cardiovascular Disease (CVD) risk. We developed a new dictionary of CVD-related keywords by analyzing emotions expressed in tweets. Tweets from eighteen US states, including the Appalachian region, were collected. Using the VADER model for sentiment analysis, users were classified as potentially at CVD risk. Machine Learning (ML) models were employed to classify individuals' CVD risk and applied to a CDC dataset with demographic information to make the comparison. Performance evaluation metrics such as Test Accuracy, Precision, Recall, F1 score, Mathew's Correlation Coefficient (MCC), and Cohen's Kappa (CK) score were considered. Results demonstrated that analyzing tweets' emotions surpassed the predictive power of demographic data alone, enabling the identification of individuals at potential risk of developing CVD. This research highlights the potential of Natural Language Processing (NLP) and ML techniques in using tweets to identify individuals with CVD risks, providing an alternative approach to traditional demographic information for public health monitoring.