Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

Abstract

The recent large outbreak of infectious diseases, such as influenza-like illnesses and COVID-19, has resulted in a flood of health-related posts on the Internet in general and on social media in particular, in a wide range of languages and dialects around the world. The obvious relationship between the number of infectious disease cases and the number of social media posts prompted us to consider how we can leverage such health-related content to detect the emergence of diseases, particularly influenza-like illnesses, and foster disease surveillance systems. We used Algerian Arabic posts as a case study in our research. From data collection to content classification, a complete workflow was implemented. The main contributions of this work are the creation of a large corpus of Arabic Facebook posts based on Algerian dialect and the proposal of a new classification model based on sentiment analysis and one-dimensional convolutional neural networks. The proposed model categorizes Facebook posts based on the users’ feelings. To counteract data imbalance, two techniques have been considered, namely, SMOTE and random oversampling (ROS). Using a 5-fold cross-validation, the proposed model outperformed other baseline and state-of-the-art models such as SVM, LSTM, GRU, and BiLTSM in terms of several performance metrics.

Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases

“…Figure 16 illustrates that our proposed graph MLP and RF models surpassed other models from related studies in accuracy. It is evident from Figure 16 that we achieved better accuracy in influenza detection compared to previous best model [ 11 ]. Moreover, we achieved superior accuracy for hepatitis detection than [ 18 ], as they achieved 99.9% with the RF model and the same dataset.…”

“…Another case involves just 20 healthy adults, raising questions about the generalizability of its results [ 10 ]. In one instance, a study used a wearable device to monitor heart rates and activity levels but did not compare its performance to other diagnostic tests for influenza [ 11 ]. An issue was also found in another machine-learning (ML) model that applied synthetic patient data, rather than real patient data [ 12 ] and necessitating further validation.…”