A multimodal deep learning framework for symptom-based disease prediction and clinical decision support

This paper presents AyuSeva, a multimodal clinical decision support system integrating the novel D2B2C-IIFNN architecture (DenseNet, Dual Attention, Bidirectional Long Short-Term Memory, One-Dimensional Convolution with Intra-Inter Fusion Neural Network) with a confidence-gated generative conversational module for symptom-based disease diagnosis from structured and unstructured clinical inputs. The Symptom Model processes 132 clinically ordered binary features through hierarchical DenseNet encoding, dual attention saliency weighting, and BiLSTM-driven bidirectional context modelling across 41 disease categories. The NLP Model maps unstructured clinical narratives into a 256-dimensional semantic space via Embedding with SpatialDropout1D, hierarchical dual Conv1D blocks, BiLSTM, dot-product Attention, and a dual-path fusion (Flatten + GlobalAveragePooling1D) producing a 2,560-dimensional hybrid representation across 55 disease categories. Under rigorous 5-fold stratified cross-validation with fresh model instantiation per fold, the Symptom Model achieves 99.52% ± 1.08% test accuracy (macro-F1: 0.9935, ROC-AUC: 0.9999, ECE: 0.0244, Brier: 0.0224) and the NLP Model achieves 93.91% ± 0.94% (macro-F1: 0.9063, ROC-AUC: 0.9975, ECE: 0.0329, Brier: 0.0958). Probability calibration analysis, confidence threshold validation with tripartite criteria (safety, reliability, efficiency), and asymmetric severity-weighted cost modelling collectively establish the 95% confidence threshold as an empirically validated, non-arbitrary, Pareto-optimal decision boundary, achieving zero acute false-negative rate for structured inputs and perfect Critical-tier recall (1.0000) on both diagnostic pathways. To bridge diagnostic inference with patient-centric care, AyuSeva incorporates a confidence-gated generative conversational model. Through domain-constrained contextual conditioning and structured semantic segmentation, it generates high-confidence, empathetic clinical guidance addressing triage, treatment, lifestyle modification, and emergency protocols. The system leverages a memory-enabled real-time interface to facilitate interactive, personalised dialogues while enforcing strict medical safety and ethical standards. Extensive ablation studies confirm the synergistic importance of BiLSTM and dual attention mechanisms in resolving semantic ambiguity and optimising feature representation. Benchmark comparisons against state-of-the-art algorithms, including Temporal Convolutional Networks (TCN), Multilayer Perceptron’s (MLP), Gated Recurrent Units (GRU), and Random Forest, validate AyuSeva’s superior architectural robustness, generalisability, and training stability. With a scalable design suitable for telemedicine and rural outreach, AyuSeva redefines healthcare artificial intelligence by merging algorithmic depth with human-centred design, setting a new standard for intelligent, ethical, and empathetic clinical assistants.