Advancing Personalized Diagnosis and Therapeutics Using Deep Learning Architecture

Rahat Ullah ^1* Nadeem Sarwar ² Mohammed Naif Alatawi ³ Abeer Alsadhan ⁴ Hathal Salamah Alwageed ⁵ Maqbool Khan ⁶ Aitizaz Ali ^7*

• 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.