Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mahalaxmi, S.B.K.U.; Kala, I.; Sagar, Lalit Kumar; Thomas, N.; Kaushal, Ashish Kumar; Robert, Nismon Rio Title A name given to the resource TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Informing Science;Volume;28;Issue;;Article No.;Article 16; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.28945/5443" target="_blank" rel="noreferrer noopener">https://doi.org/10.28945/5443</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000470882?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000470882?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mahalaxmi S.B.K.U., Department of Electronics and Communication Engineering, Aditya College of Engineering and Technology, Andhra Pradesh, Surampalem, India; Kala I., Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India; Sagar L.K., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Uttar Pradesh, Ghaziabad, India; Thomas N., Department of Chemistry, St Mother Theresa Engineering College, Vagaikulam, Tamil Nadu, Thoothukudi, India; Kaushal A.K., Department of Jindal Global Business School, O P Jindal Global University, Haryana, Sonepat, India; Robert N.R., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource Aim/Purpose In this research work, we have developed a predictive model that focuses on utilizing knowledge from the related domains. Background A serious public health issue, especially in tropical and subtropical regions, is dengue fever, a viral infection passed by mosquitoes. Accurate early prediction of disease outcomes is essential for both efficient patient management and ef-fective use of resources. More complex methods are required since conven-tional prediction models could be faulty with limited labeled data and complex feature interactions. Methodology We propose a new strategy integrating deep attention mechanisms with trans-fer learning to enhance prediction modeling of dengue disease outcomes. First pre-trained on a large, linked dataset of common viral illnesses, a deep neural network enables the model to learn generic properties. We then iteratively im-prove our pre-trained model using a specific dengue dataset. Incorporating a deep attention mechanism allows for the focus on the most relevant features, improving interpretability and accuracy. Contribution Among logistic regression, random forests, and basic deep learning methods, current models reveal poor accuracy and dependability in forecasting dengue disease outcomes. These models sometimes fail to sufficiently depict the com-plicated interactions among clinical variables, especially under conditions with limited data. Findings The proposed method outperforms more traditional models pretty strongly. Our model acquired in the training phase an accuracy of 0.92, precision of 0.91, recall of 0.90, and F1-score of 0.90. It maintained high performance on testing with an accuracy of 0.91, precision of 0.90, recall of 0.89, and an F1-score of 0.89. Similar patterns were indicated by an accuracy of 0.90, precision of 0.89, recall of 0.88, and an F1-score of 0.88 validation results. The model also demonstrated a lowered loss (0.21, 0.23, 0.24 in training, testing, and vali-dation, respectively), higher true positive rates (0.90, 0.89, 0.88), and lower false positive rates (0.10, 0.11, 0.12). Deep attention methods and transfer learning offer a robust and effective strategy for predictive modeling of dengue disease outcomes, therefore considerably boosting accuracy and dependability. This approach offers considerable possibilities for dengue-endemic patient manage-ment and resource allocation. Recommendations for Researchers Investigations should prioritize the validation of the algorithm in various healthcare environments to assess its efficacy in clinical application. Future Research In future research, this work can be enhanced using several deep learning algo-rithms to achieve better accuracy and performance. This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes. Subject The topic of the resource deep attention mechanisms; dengue prediction; healthcare informatics; predictive an-alytics; transfer learning Publisher An entity responsible for making the resource available Informing Science Institute Relation A related resource ISSN: 15479684; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online