ArticleMethodsX2023
Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke.
Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors at 2 institutions in 1 country.
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Abstract
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
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