Fuzzy min-max dual motif-guided heterogeneous Dandelion graph multi-scale residual attention network for healthcare AI misclassification reduction.

The rapid advancement of Artificial Intelligence (AI) in healthcare diagnostics has significantly improved disease detection and treatment prediction; however, misclassification remains a persistent challenge due to data heterogeneity, incomplete clinical information, and complex feature interdependencies. To address these limitations, a novel framework titled Dual Motif Guided Heterogeneous Interactive Dandelion Graph Multi-Scale Residual Attention Network (DMGH-IDGM-SRAN) is proposed for Reducing Misclassifications and Enhancing Model Performance in Healthcare AI. The study employs a curated dataset, "Healthcare AI Dataset for Reducing Misclassifications and Enhancing Model Performance in Healthcare AI", collaboratively developed by technical and medical experts, consisting of 743 clinical records obtained from the Pulmonology Department of one of India's leading hospitals. Pre-processing is conducted using the Fuzzy Min-Max Neural Network (FMNN) to handle uncertainty and overlapping class boundaries effectively. Subsequently, Adaptive Causal Decision Transformers (AdaCred) are utilized for feature extraction, capturing causal dependencies and temporal associations across heterogeneous clinical attributes. The proposed DMGH-IDGM-SRAN integrates a Dual-Attention-Guided Interactive Multi-Scale Residual Network (DA-IMRN) with a Motif-Based Heterogeneous Graph Attention Network (MBHAN), whose hyperparameters are fine-tuned using the Dandelion Optimizer (DO) to enhance model convergence and stability. Experimental evaluation demonstrates that the proposed model achieves an exceptional 99.9% classification accuracy, significantly outperforming existing approaches. The framework offers two key advantages: (i) it effectively captures complex cross-feature relationships between clinical and biochemical parameters, and (ii) it provides robust and interpretable diagnostic predictions under noisy and incomplete data conditions.