3-D EM Modeling of Medical Microwave Imaging Scenarios With Controllable Accuracy

Singh, Tushar; Ninkovic, Branislav; Tasić, Miodrag; Stevanovic, Marija Nikolic; Kolundžija, Branko M.

doi:10.1109/tap.2022.3209244

Cited by 6 publications

(1 citation statement)

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“…Addressing stroke as a major contributor to lifelong disability and mortality necessitates urgent intervention within hours of symptom onset [6][7][8] . Effective treatment pivots on accurately identifying the stroke type-whether ischemic (IS) from disrupted brain blood supply or intracranial hemorrhage (ICH) due to skull bleeding [9][10][11][12] . Timely hospital arrival and treatment within 3-5 hours significantly enhance survival rates and minimize long-term brain damage among stroke patients [13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

OptiSelect and EnShap: Integrating Machine Learning and Game Theory for Ischemic stroke prediction

Chakraborty,

Bandyopadhyay,

Parui

et al. 2024

Preprint

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This paper presents a novel approach to predictive Ischemic brain stroke analysis using game theory and machine learningtechniques. The study investigates the use of the Shapley value in predictive Ischemic brain stroke analysis. Initially, preferencealgorithms identify the most important features in various machine learning models, including logistic regression, K-nearestneighbor, decision tree, support vector machine (linear kernel), support vector machine (RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley Valuemethod has been used to rank the models using their best four features based on their predictive capabilities. As a result,better-performing models have been found. Afterward, ensemble machine learning methods were used to find the mostaccurate predictions using the top 5 models ranked by shapely value. The research demonstrates an impressive accuracyof 92.39%, surpassing other proposed models’ performance. This study highlights the utility of combining game theory andmachine learning in Ischemic brain stroke prediction and the potential of ensemble learning methods to increase predictiveaccuracy in Ischemic stroke analysis.

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