Adaptive Liquid State-Space Networks: A Novel Neuro-Dynamic Architecture for Tabular Classification with Application to Anemia Diagnosis

Background/Objectives: Anemia is a complex hematological disorder with various subtypes, and its accurate diagnosis is of critical importance for clinical decision-making processes. Traditional machine learning algorithms and standard deep learning models often struggle to model the nonlinear and typically imbalanced nature of clinical tabular data, which negatively impacts the classification performance and reliability of diagnostic systems.

Methods

This study proposed ALSS-Net, a novel deep learning architecture that combines the State-Space Model (SSM), Liquid Time Constant (LTC), and Adaptive Gates mechanisms, with the aim of classifying difficult-to-diagnose types of anemia with high accuracy, robustness, and statistical reliability.

Results

Experimental results showed that the Adaptive Liquid State-Space Networks (ALSS-Net) architecture achieved an accuracy of 92.97% on the validation set. Furthermore, on the test dataset, this model obtained an accuracy of 89.13% and an F1 score of 0.8513, outperforming five of the six baseline models by a statistically significant margin (McNemar's test, p < 0.001); the difference with respect to the strongest baseline (MLP) did not reach statistical significance. The ablation studies indicated that the proposed dynamic time constant and adaptive gating mechanisms contributed to the separation of the difficult classes, with adaptive gating accounting for the larger part of the improvement.