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

Afnan Al-Ali1, Uvais Qidwai1, Saadat Kamran2

  • 1Computer Science and Engineering Department, Qatar University, Doha, Qatar.

Methodsx
|September 27, 2023
PubMed
Summary

This study introduces an AI method combining Binary Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System to accurately predict ischemic stroke infarction growth. The novel approach reduces dimensionality and training time, improving prediction efficiency.

Keywords:
ANFIS-BPSO based Prediction SystemAdaptive neuro-fuzzy inference system (ANFIS)Binary particle swarm optimization technique (BPSO)Infarction growth rate II (IGR II)Ischemic stroke

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Ischemic stroke causes brain cell death due to blocked blood flow.
  • Accurate prediction of infarction growth is crucial for managing stroke progression.
  • Existing AI methods like ANFIS face challenges with high dimensionality and long training times.

Purpose of the Study:

  • To develop an innovative, automatic method for predicting the second infarction growth after an initial CT scan in ischemic stroke patients.
  • To address the limitations of conventional ANFIS by reducing dimensionality and training time.

Main Methods:

  • A novel approach combining Binary Particle Swarm Optimization (BPSO) with Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture.
  • Selection of clinically relevant features using Pearson correlation coefficients and P-values.
  • Comparison of the proposed model against conventional ANFIS, Support Vector Regressor (SVR), shallow Neural Networks, and Linear Regression.

Main Results:

  • The developed model demonstrated high accuracy in predicting infarction growth.
  • Achieved a Root Mean Square Error of 0.091, Mean Squared Error of 0.0086, Mean Absolute Error of 0.064, and Cosine distance of 0.074.
  • The BPSO-ANFIS model showed improved efficiency in terms of reduced dimensionality and training time compared to conventional ANFIS.

Conclusions:

  • The BPSO-ANFIS model offers a more accurate and efficient solution for predicting ischemic stroke progression.
  • This method holds potential for improving clinical decision-making and patient outcomes in stroke management.
  • The study highlights the effectiveness of combining optimization algorithms with fuzzy inference systems for complex medical predictions.