Open AccessA Multilayer Residual Dendritic Neural Model for Predicting Stroke Prognosis
Author(s) -
Yuxi Wang,
Haochang Jin,
Maocheng Cao,
Xiong Xiao,
Li Wang
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3591799
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Stroke, caused by occlusion or rupture of cerebral blood vessels, is a leading cause of disability and death globally. Accurate stroke prognosis can enhance clinical decisions and rehabilitation strategies. The dendritic neural model (DNM), inspired by biological neurons, shows strong predictive capability, but struggles with real-world small-scale tabular stroke data. Therefore, an improved residual dendritic neural model (RDNM) is proposed. It contains a series of stacked synaptic and dendritic layers to enhance the power. Residual connections are added between layers to address the vanishing gradient problem. Evaluations using one public and two private stroke prognosis datasets demonstrate that RDNM significantly outperforms original DNM and state-of-the-art deep-learning methods, highlighting its potential for clinical applications. Source code is available at https://github.com/jhc050998/RDNM.
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