An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model

Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The...

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Bibliographic Details
Published in2024 4th International Conference on Sustainable Expert Systems (ICSES) pp. 1637 - 1643
Main Authors Kavitha, K., Jyotsna Devi, Vanjangi, Shabbir Alam, Mohammad, V, Sindhu, Gandhewar, Nisarg, Kalra, Gourav
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.10.2024
Subjects
Accuracy
Blood
Brain modeling
Brain Stroke Prediction
Correlation
Extreme learning machine (ELM)
Feature extraction
Numerical models
Prediction algorithms
Predictive models
Proposals
Recursive Feature Elimination (RFE)
Training
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Summary:Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%.
DOI:10.1109/ICSES63445.2024.10763097