A Machine Learning Approach to Detect the Brain Stroke Disease

Author:

Akter Bonna1,Rajbongshi Aditya2,Sazzad Sadia3,Shakil Rashiduzzaman1,Biswas Jahanur2,Sara Umme3

Affiliation:

1. Daffodil International University,Dept. of CSE,Dhaka,Bangladesh

2. Jahangirnagar University,Dept. of CSE,Dhaka,Bangladesh

3. National Institute of Textile Engineering and Research,Dept. of CSE,Dhaka,Bangladesh

Publisher

IEEE

Reference20 articles.

1. Identification of significant features and data mining techniques in predicting heart disease

2. Prediction of Cardiovascular Disease Through Cutting-Edge Deep Learning Technologies;ashraf;An Empirical Study Based on TENSORFLOW PYTORCH and KERAS International Conference on Innovative Com-puting and Communications,2021

3. Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines

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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. StrokeDNN: A Convolutional Neural Network and Gated Recurrent Unit Integrated Brain Stroke Prediction System;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05

3. Machine Learning-Based Pre-Stroke Detection System;2024 International Conference on Science Technology Engineering and Management (ICSTEM);2024-04-26

4. Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey;2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS);2024-04-18

5. Detection of Brain Stroke Using Machine Learning Algorithm;2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT);2024-03-15

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