ItemArticle

TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES

Title
TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES
Creator
Mahalaxmi, S.B.K.U.; Kala, I.; Sagar, Lalit Kumar; Thomas, N.; Kaushal, Ashish Kumar; Robert, Nismon Rio
Description
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.
Source
Informing Science;Volume;28;Issue;;Article No.;Article 16;
Date
01-01-2025
Publisher
Informing Science Institute
Subject
deep attention mechanisms; dengue prediction; healthcare informatics; predictive an-alytics; transfer learning
Coverage
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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 15479684;
Format
online
Language
English
Type
Article

Citation

Mahalaxmi, S.B.K.U.; Kala, I.; Sagar, Lalit Kumar; Thomas, N.; Kaushal, Ashish Kumar; Robert, Nismon Rio, “TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23451.

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