Enhancing trustworthiness of Arabic online health information quality evaluation using an enhanced BERT architecture with PCA and ICA feature weighting.

Despite the exponential increase in the availability of online health information, its quality remains questionable, presenting a significant challenge to address. This study addresses this issue by using artificial intelligence techniques, such as deep learning, to evaluate the quality of health information and to mimic human-level evaluation capabilities. The key methodologies used in the study included an enhanced version of Arabic BERT for medical data, feature extraction techniques incorporating Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and modified loss functions using information entropy to improve the model's certainty and calibration during document classification. The results of the study were encouraging: the proposed PCA-based model achieved higher accuracy than the competing models and reached 94.7% on the dataset used, comparable to reported human-level performance. Finally, these findings may contribute to improving the reliability of online health information in Arabic contexts and provide a foundation for future efforts aimed at supporting healthcare decision-making. The methodologies and results presented here offer policymakers and researchers valuable tools to assess and ensure the trustworthiness of online health information.