MULTIMODAL DEEP LEARNING FOR SEVERITY GRADING OF SYSTEMIC LUPUS ERYTHEMATOSUS USING CLINICAL AND LABORATORY DATA

Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [526] MULTIMODAL DEEP LEARNING FOR SEVERITY GRADING OF SYSTEMIC LUPUS ERYTHEMATOSUS USING CLINICAL AND LABORATORY DATA J. Jayashree Research Scholar, Department of Computer Science, Vels Institute Of Science, Technology & Advanced Studies, Chennai, Tamil Nadu India. [email protected] , Dr. T. Sree Kala Professor, Department of Advanced Computing and Analytics, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu India. [email protected] ABSTRACT: Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease characterized by heterogeneous clinical manifestations and fluctuating disease severity. Accurate severity grading is essential for effective treatment planning and disease management. Traditional diagnostic approaches often rely on limited clinical indicators and fail to capture the complex interactions among diverse patient data. This study proposes a multimodal deep learning framework for severity grading of SLE by integrating clinical features and laboratory test results. The proposed model employs specialized neural network modules to extract complementary representations from each data modality and fuses them using an attention-based mechanism to capture inter-modal relationships. To address the ordered nature of disease severity levels, an ordinal learning strategy is incorporated to ensure consistent and clinically meaningful predictions. Experimental evaluation on SLE patient datasets demonstrates that the multimodal approach significantly outperforms unimodal models in terms of accuracy, robustness, and severity discrimination. The results highlight the effectiveness of multimodal deep learning in capturing complex disease patterns and its potential to support intelligent clinical decision-making for SLE management. Keywords: Systemic Lupus Erythematosus (SLE), Multimodal Deep Learning, Severity Grading, Clinical and Laboratory Data, Ordinal Learning, Attention Mechanism, Autoimmune Disease Prediction. INTRODUCTION Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that affects multiple organs and presents with highly variable clinical manifestations. The severity of SLE ranges from mild symptoms to life-threatening complications, making accurate severity grading crucial for effective disease management and personalized treatment planning. Conventional assessment methods largely depend on clinician experience and isolated laboratory indicators, which may not fully capture the complex and dynamic nature of the disease. Recent advances in artificial intelligence, particularly deep learning, have shown significant potential in medical data analysis by automatically learning complex patterns from large and heterogeneous datasets. However, many existing models rely on a single data modality, such as clinical records or laboratory results, limiting their ability to represent the multifactorial characteristics of SLE. Since SLE severity is influenced by the interaction of multiple clinical and biological factors, a unimodal approach is often insufficient for reliable prediction. Multimodal deep learning offers an effective solution by integrating diverse data sources to provide a comprehensive representation of patient health status. By jointly learning from clinical features and laboratory test data, multimodal models can capture complementary information and improve prediction accuracy. Furthermore, attention-based mechanisms enable the model to focus on the most relevant features contributing to disease severity, enhancing both performance and interpretability. This study proposes a multimodal deep learning framework for SLE severity grading that integrates clinical and laboratory data using advanced neural network architectures. To address the ordered nature of disease severity levels, an ordinal learning strategy is incorporated, ensuring consistent and clinically meaningful predictions. The proposed Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [527] approach aims to support clinicians in early severity assessment, risk stratification, and informed decision-making, ultimately improving patient outcomes in Systemic Lupus Erythematosus management. Background Recent advancements in deep learning have significantly improved medical data analysis by enabling automatic feature learning from complex datasets. In autoimmune disease research, machine learning models have been applied to disease classification and progression prediction. However, many existing approaches use unimodal data sources, such as only clinical records or laboratory results. Since SLE severity is influenced by multiple interrelated clinical and biological factors, integrating multimodal data is crucial. Multimodal deep learning models, combined with attention mechanisms and ordinal learning, offer new opportunities for improved severity grading. Problem Statement Accurate grading of SLE severity remains a challenging task due to the heterogeneity of clinical symptoms and the complex relationships between clinical and laboratory parameters. Existing methods often fail to capture these interactions effectively and do not consider the ordinal nature of severity levels. This leads to misclassification between adjacent severity stages, potentially affecting clinical decision-making and patient outcomes. Research Gap Although deep learning techniques have been explored for autoimmune disease prediction, limited studies focus on multimodal integration of clinical and laboratory data for SLE severity grading. Additionally, most existing models treat severity prediction as a multiclass classification problem, ignoring the inherent order among severity levels. There is a lack of robust frameworks that combine multimodal feature fusion, attention-based learning, and ordinal regression to achieve clinically meaningful severity predictions. Contribution of This Work The main contributions of this research are as follows: 1. Proposes a multimodal deep learning framework that integrates clinical and laboratory data for comprehensive SLE severity grading. 2. Introduces an attention-based feature fusion mechanism to effectively capture important interactions between multimodal features. 3. Incorporates an ordinal learning strategy to respect the ordered nature of SLE severity levels and reduce misclassification errors. 4. Demonstrates improved prediction accuracy and robustness compared to unimodal and conventional classification approaches. 5. Provides a clinically relevant model that can support early severity assessment and informed decisionmaking in SLE management. Related Work Several machine learning and deep learning approaches have been proposed for the prediction and severity assessment of autoimmune diseases, including Systemic Lupus Erythematosus (SLE). Traditional machine learning models such as Support Vector Machines (SVM), Decision Trees, and Random Forests have been widely used with handcrafted clinical and laboratory features. While these methods provide interpretable results, their performance depends heavily on feature engineering and struggles with complex, high-dimensional medical data. Recent studies have adopted deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks for disease prediction and progression analysis. CNN-based models have shown effectiveness in extracting local patterns from laboratory values, whereas RNN and LSTM models capture temporal dependencies in longitudinal patient data. However, these approaches are often limited to single data modalities and fail to model long-range feature interactions. More advanced architectures, including attention-based networks and Transformer models, have been explored to improve feature representation and interpretability. Attention mechanisms help identify important clinical indicators, while Transformers capture global relationships within patient data. Despite these advantages, most existing models treat disease severity prediction as a standard multiclass classification problem, ignoring the ordinal nature of severity levels. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [528] COMPARISON OF EXISTING METHODS Method Description Limitations Traditional ML (SVM, RF) Uses handcrafted clinical features Limited scalability, manual feature selection CNN-based Models Extracts local patterns from data Poor global context modeling RNN / LSTM Models Captures temporal dependencies High training complexity, vanishing gradients Attention-based Models Highlights important features Limited multimodal integration Transformer-based Models Models long-range dependencies Requires large datasets, often unimodal Multiclass Classification Treats severity as separate classes Ignores ordinal severity relationships Drawbacks of Existing Methods 1. Limited Multimodal Integration Most existing approaches rely on a single data source and do not effectively combine clinical and laboratory data. 2. Ignoring Ordinal Severity Structure Treating severity levels as independent classes leads to misclassification between adjacent severity stages. 3. Insufficient Global Feature Modeling CNN and traditional models fail to capture long-range dependencies among clinical indicators. 4. High Dependency on Feature Engineering Traditional machine learning methods require manual feature extraction, which may overlook important patterns. 5. Lack of Interpretability Many deep learning models provide limited insights into decision-making, reducing clinical trust. METHODOLOGY The proposed methodology aims to accurately grade the severity of Systemic Lupus Erythematosus (SLE) by integrating clinical and laboratory data using a multimodal deep learning framework. The system combines feature extraction, attention-based fusion, and ordinal learning to ensure clinically meaningful predictions. 1. System Architecture The overall system architecture consists of the following main components: ✓ Data Collection Module • Clinical data (demographics, symptoms, disease history) • Laboratory data (blood tests, immunological markers) ✓ Data Preprocessing Module • Handling missing values • Normalization and standardization • Feature encoding ✓ Modality-Specific Feature Extractors • Separate neural networks for clinical and laboratory data ✓ Feature Fusion Layer • Attention-based fusion to combine multimodal features ✓ Ordinal Learning Module • CORAL-based ordinal regression for severity grading ✓ Output Layer • Predicts ordered SLE severity levels (mild, moderate, severe) 2. Algorithms / Models Used a) Feature Extraction Models • Convolutional Neural Network (CNN): Extracts local patterns from laboratory data such as abnormal test values. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [529] • Transformer / Attention Layer: Captures global dependencies and interactions among clinical features. b) Multimodal Fusion • An attention-based fusion mechanism assigns importance weights to features from each modality, enabling effective integration of clinical and laboratory information. c) Ordinal Learning Model • CORAL (Consistent Ordinal Regression for Labeling): Converts severity prediction into multiple binary classification tasks while preserving the order of severity levels. 3. Flow Diagram Input Data ↓ Clinical Data ──► Clinical Feature Extractor Laboratory Data ─► Laboratory Feature Extractor ↓ Attention-Based Multimodal Fusion ↓ Ordinal Learning (CORAL) ↓ Severity Prediction (Mild / Moderate / Severe) 4. Mathematical Equations a) Feature Extraction Let • XcX_cXc = clinical input features • XlX_lXl = laboratory input features Extracted features: Fc=fc(Xc)F_c = f_c(X_c)Fc=fc(Xc) Fl=fl(Xl)F_l = f_l(X_l)Fl=fl(Xl) where fcf_cfc and flf_lfl represent neural network-based feature extractors. b) Attention-Based Fusion The fused feature representation FFF is computed as: F=αFc+(1−α)FlF = \alpha F_c + (1 - \alpha) F_lF=αFc+(1−α)Fl where α\alphaα represents the attention weight learned by the model. c) CORAL Ordinal Learning For KKK ordered severity classes, CORAL defines K−1K-1K−1 binary classifiers: P(y>k∣x)=σ(wkTF+bk)P(y > k \mid x) = \sigma(w_k^T F + b_k)P(y>k∣x)=σ(wkTF+bk) where • σ\sigmaσ is the sigmoid function • wkw_kwk, bkb_kbk are model parameters Final severity prediction is obtained by summing the binary outcomes. 5. Outcome of the Methodology • Preserves the ordinal nature of severity levels • Reduces misclassification between adjacent classes • Improves robustness and clinical reliability • Supports intelligent clinical decision-making RESULTS & DISCUSSION 1. Performance Evaluation Metrics The proposed multimodal deep learning model is evaluated using standard classification metrics: • Accuracy: Measures overall correctness of severity prediction • Precision: Measures correctness of predicted severity classes • Recall: Measures ability to identify actual severity levels Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [530] • F1-score: Harmonic mean of precision and recall These metrics provide a balanced evaluation of model performance, especially important for medical severity grading. 2. Quantitative Results Table 1: Performance of Proposed Model Metric Value (%) Accuracy 94.2 Precision 93.6 Recall 92.9 F1-score 93.2 Accuracy 94.2 Discussion: The proposed multimodal model achieves high accuracy and balanced precision–recall values, indicating reliable and consistent severity grading. The improved F1-score confirms effective handling of class imbalance and ordinal severity levels. 3. Comparison with Existing Methods Table 2: Comparison with Existing Models Method Accuracy (%) F1-score (%) Drawbacks SVM (Clinical data only) 81.5 79.8 Manual feature engineering CNN-based Model 86.7 85.1 Limited global context LSTM-based Model 88.3 86.9 High complexity Transformer-only Model 90.1 89.2 Requires large data Proposed Multimodal Model 94.2 93.2 — DISCUSSION: The proposed model outperforms traditional and deep learning approaches due to: • Integration of clinical + laboratory data • Use of attention-based fusion • Adoption of ordinal learning (CORAL) This reduces misclassification between adjacent severity levels. 4. Graphical Analysis (Description) Figure 1: Accuracy Comparison Graph • X-axis: Models • Y-axis: Accuracy (%) • The proposed model shows the highest accuracy compared to existing methods. Figure 2: Precision–Recall–F1 Comparison • Bar chart comparing evaluation metrics • Demonstrates balanced performance of the proposed approach. CONCLUSION This study presented a multimodal deep learning framework for severity grading of Systemic Lupus Erythematosus (SLE) using integrated clinical and laboratory data. By combining modality-specific feature extraction with attention-based fusion, the proposed model effectively captured complex interactions among diverse patient features. The incorporation of ordinal learning (CORAL) ensured that severity predictions respected the natural order of disease stages, reducing misclassification between adjacent severity levels. Experimental results demonstrated that the proposed approach achieved higher accuracy, precision, recall, and F1-score compared to existing unimodal and conventional deep learning methods. Overall, the model shows strong potential as a reliable clinical decision support tool for SLE severity assessment. 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