Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering

2026-05-17

AI Summary

This study addresses the challenges of noise sensitivity and feature preservation in diagnosing multiple neurological disorders from sparse MRI data by proposing a purely classical, quantum-inspired tensor network framework. For the first time, the approach integrates PARAFAC CANDECOMP/PARAFAC (CP) tensor decomposition with an ensemble classifier for large-scale multiclass neurodegenerative disease prediction. The model’s expressiveness and robustness are enhanced through controlled tensor rank selection, and its performance is rigorously evaluated using five-fold nested stratified cross-validation. Evaluated on a balanced clinical dataset comprising 55,160 images across eight diagnostic classes, the proposed method achieves performance comparable to or better than current state-of-the-art classical approaches, demonstrating its effectiveness and potential in medical image analysis.

Abstract

Accurate diagnosis of neurological disorders is contingent upon advanced imaging modalities such as Magnetic Resonance Imaging (MRI), which commonly utilize sparse imaging techniques to reconstruct images from limited data, thus reducing storage and acquisition time. However, challenges remain in managing noise and preserving critical diagnostic features for effective analysis. In this study, an ensemble classifier is enriched with PARAFAC CP tensor decompositions, drawing mathematical inspiration from quantum neural network architectures but implemented entirely classically. The model was evaluated on a large, balanced clinical dataset comprising 55,160 images across 8 diagnostic categories, employing both higher and lower PARAFAC rank configurations. Evaluated through 5-fold nested stratified cross-validation, both configurations achieved strong validation performance, demonstrating robustness to tensor network expressivity. Additionally, the proposed model achieved competitive performance relative to recent classical approaches, further underscoring the potential of quantum-inspired classical frameworks to enhance medical image analysis and support reliable clinical diagnosis. Future work will explore the integration of advanced encoding schemes, deployment on real quantum hardware, and the use of more diverse neurological datasets.

Problem

• neurological disorder prediction
• multi-class classification
• medical image analysis
• sparse imaging
• diagnostic feature preservation

Innovation

• tensor network
• PARAFAC decomposition
• quantum-inspired classical model
• neurological disorder prediction
• medical image analysis