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Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke

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submitted on 2024-01-30, 07:31 and posted on 2024-01-30, 07:32 authored by Afnan Al-Ali, Uvais Qidwai, Saadat Kamran

Ischemic stroke, a severe medical condition triggered by a blockage of blood flow to the brain, leads to cell death and serious health complications. One key challenge in this field is accurately predicting infarction growth - the progressive expansion of damaged brain tissue post-stroke. Recent advancements in artificial intelligence (AI) have improved this prediction, offering crucial insights into the progression dynamics of ischemic stroke. One such promising technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS), has shown potential, but it faces the 'curse of dimensionality' and long training times as the number of features increased. This paper introduces an innovative, automatic method that combines Binary Particle Swarm Optimization (BPSO) with ANFIS architecture, achieves reduction in dimensionality by reducing the number of rules and training time. By analyzing the Pearson correlation coefficients and P-values, we selected clinically relevant features strongly correlated with the Infarction Growth Rate (IGR II), extracted after one CT scan. We compared our model's performance with conventional ANFIS and other machine learning techniques, including Support Vector Regressor (SVR), shallow Neural Networks, and Linear Regression. 

• Inputs: Real data about ischemic stroke represented by clinically relevant features. 

• Output: An innovative model for more accurate and efficient prediction of the second infarction growth after the first CT scan. 

• Results: The model achieved commendable statistical metrics, which include a Root Mean Square Error of 0.091, a Mean Squared Error of 0.0086, a Mean Absolute Error of 0.064, and a Cosine distance of 0.074.

Other Information

Published in: MethodsX
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.mex.2023.102375

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • College of Arts and Sciences - QU
  • Hamad Medical Corporation