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Advancing Personalized Diagnosis and Therapeutics Using Deep Learning Architecture – Frontiers
ORIGINAL RESEARCH article
Front. Med.
Sec. Precision Medicine
Volume 12 – 2025 | doi: 10.3389/fmed.2025.1545528
This article is part of the Research Topic Integrating AI and Machine Learning in Advancing Patient Care: Bridging Innovations in Mental Health and Cognitive Neuroscience View all articles
- 1 Nanjing University of Information Science and Technology, Nanjing, China
- 2 Bahria University, Islamabad, Islamabad, Pakistan
- 3 University of Tabuk, Tabuk, Tabuk, Saudi Arabia
- 4 Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
- 5 Jouf University, Sakakah, Al Jawf, Saudi Arabia
- 6 Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, Khyber Pakhtunkhwa, Pakistan
- 7 Asia Pacific University Malaysia, Malaysia, Malaysia
Autoimmune disorders (AID) present significant challenges due to their complex causes and varied clinical symptoms. Traditional diagnostic methods rely on symptom observation and biomarker detection, but they often lack specificity and fail to provide personalized treatment options. This study proposes ImmunoNet, a deep learning-based framework that integrates genetic, molecular, and clinical data to enhance the accuracy of autoimmune disease diagnosis and treatment. ImmunoNet leverages convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) to analyze large-scale datasets, enabling precise disease classification and treatment recommendations. The model improves interpretability through explainable AI techniques and enhances privacy via federated learning. Comparative evaluations demonstrate that ImmunoNet outperforms traditional machine learning models, achieving a 98% accuracy rate in predicting autoimmune disorders. By advancing precision medicine in immunology, this approach provides clinicians with a powerful tool for personalized diagnosis and optimized therapeutic strategies.
Keywords: deep learning, Autoimmune disorder, ensemble learning, CNN, MLP
Received: 15 Dec 2024; Accepted: 11 Mar 2025.
Copyright: © 2025 Ullah, Sarwar, Alatawi, Alsadhan, Salamah Alwageed, Khan and Ali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Rahat Ullah, Nanjing University of Information Science and Technology, Nanjing, China
Aitizaz Ali, Asia Pacific University Malaysia, Malaysia, Malaysia
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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