A Hybrid Model for Early Prediction of Stroke Disease-Reference-Cited by-同舟云学术

A Hybrid Model for Early Prediction of Stroke Disease

Published:2022-10-13 Issue: Volume: Page:
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Container-title:2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
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Author:

Dhiman Heena¹,Kumar Rajneesh¹,Rani Pooja²

Affiliation:

1. MM Engineering College, Maharishi Markandeshwar (Deemed to be University),Department of CSE,Mullana

2. MMICTBM Maharishi Markandeshwar (Deemed to be University),Mullana

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9964473/9964406/09965122.pdf?arnumber=9965122

Reference16 articles.

1. AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

2. Stroke Prediction Analysis using Machine Learning Classifiers and Feature Technique;islam;International Journal of Electronics and Communication,2021

3. Improving the classification accuracy of melanoma detection by performing feature selection using binary Harris hawks optimization algorithm

4. A survey of decision tree classifier methodology

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1. Machine-Learning Application for Detection and Prediction of Stroke Risk and Post-Stroke Outcomes: A Comprehensive Review;2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD);2024-08-01

2. Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application;Journal of Imaging;2024-07-02

3. A Systematic Review on Role of AI and IOT in Prediction and Rehabilitation Model for Brain Stroke;2023 6th International Conference on Contemporary Computing and Informatics (IC3I);2023-09-14

4. Stroke Factor Analysis based on Granger Causality Test;Highlights in Science, Engineering and Technology;2023-04-01