Ensemble Classifier for Stroke Prediction with Recurshive Feature Elimination

Authors

  • Pooja Mitra  Research Scholar, Sarvajanik college of engineering and technology, Surat, Gujarat, India
  • Dr. Sheshang Degadwala  Associate Professor & Head of Department, Dept. of Comp. Engineering, Sigma University, Gujarat, India
  • Dhairya Vyas  Research Scholar, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT2390430

Keywords:

Stroke, Ensemble Classifier, Attribute Elimination, Medical Diagnostics, Classification Accuracy, Feature Selection

Abstract

This research proposes an ensemble classifier approach for stroke prediction utilizing Recursive Feature Elimination (RFE). By iteratively selecting and excluding features, RFE enhances the model's predictive capacity while minimizing overfitting. The ensemble classifier, formed by combining diverse base classifiers, capitalizes on their complementary strengths to enhance overall predictive performance. Leveraging a comprehensive dataset, the proposed approach demonstrates superior stroke prediction accuracy compared to individual classifiers, underscoring its potential as an effective tool for early stroke risk assessment.

References

  1. S. K. Satapathy, A. Patel, P. Yadav, Y. Thacker, D. Vaniya, and D. Parmar, “Machine Learning Approach for Estimation and Novel Design of Stroke Disease Predictions using Numerical and Categorical Features,” in 2023 International Conference for Advancement in Technology (ICONAT), 2023, pp. 1–6. doi: 10.1109/ICONAT57137.2023.10080722.
  2. K. S. R. S, B. Chandra, K. Kausalya, C. RM, and G. R. V, “Prognosis of Stroke using Machine Learning Algorithms,” in 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 2023, pp. 1–6. doi: 10.1109/ICCMC56507.2023.10084158.
  3. C. H. Patel, D. Undaviya, H. Dave, S. Degadwala, and D. Vyas, “EfficientNetB0 for Brain Stroke Classification on Computed Tomography Scan,” in 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2023, pp. 713–718. doi: 10.1109/ICAAIC56838.2023.10141195.
  4. N. R. Kifli, H. Hidayat, Rahmawati, F. P. Sukoco, A. R. Yuniarti, and S. Rizal, “Brain Stroke Classification using One Dimensional Convolutional Neural Network,” in 2022 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), 2022, pp. 1–6. doi: 10.1109/APWiMob56856.2022.10014207.
  5. Z. B. Feliandra, S. Khadijah, M. F. Rachmadi, and D. Chahyati, “Classification of Stroke and Non-Stroke Patients from Human Body Movements using Smartphone Videos and Deep Neural Networks,” in 2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2022, pp. 187–192. doi: 10.1109/ICACSIS56558.2022.9923501.
  6. A. N. Tusher, M. S. Sadik, and M. T. Islam, “Early Brain Stroke Prediction Using Machine Learning,” in 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), 2022, pp. 1280–1284. doi: 10.1109/SMART55829.2022.10046889.
  7. N. N., P. R. J, and S. N. M, “Analysis and Classification of Occurrence of Brain Stroke Types using Machine Learning Approaches,” in 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 2022, pp. 1–5. doi: 10.1109/MysuruCon55714.2022.9972403.
  8. A. P. Ponselvakumar, S. Nivetha, and M. Nevithaprakasini, “Risk Detection of Stroke Using Classifier Algorithms,” in 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT), 2022, pp. 19–24. doi: 10.1109/CCiCT56684.2022.00016.
  9. D. I. Puspitasari, A. F. R. Kholdani, A. Dharmawati, M. E. Rosadi, and W. M. P. Dhuhita, “Stroke Disease Analysis and Classification Using Decision Tree and Random Forest Methods,” in 2021 Sixth International Conference on Informatics and Computing (ICIC), 2021, pp. 1–4. doi: 10.1109/ICIC54025.2021.9632906.
  10. X. Li, H. Xu, M. Li, and D. Zhao, “Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data,” in 2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB), 2021, pp. 135–139. doi: 10.1109/ICBCB52223.2021.9459205.
  11. V. JalajaJayalakshmi, V. Geetha, and M. M. Ijaz, “Analysis and Prediction of Stroke using Machine Learning Algorithms,” in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1–5. doi: 10.1109/ICAECA52838.2021.9675545.
  12. T. Badriyah, N. Sakinah, I. Syarif, and D. R. Syarif, “Machine Learning Algorithm for Stroke Disease Classification,” in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020, pp. 1–5. doi: 10.1109/ICECCE49384.2020.9179307.
  13. Indarto, E. Utami, and S. Raharjo, “Mortality Prediction Using Data Mining Classification Techniques in Patients With Hemorrhagic Stroke,” in 2020 8th International Conference on Cyber and IT Service Management (CITSM), 2020, pp. 1–5. doi: 10.1109/CITSM50537.2020.9268802.
  14. X. Lin, Y. Chen, Z. Gong, and H. Zhang, “Brain Stroke Classification using a Microwave Transmission Line Approach,” in 2020 IEEE Asia-Pacific Microwave Conference (APMC), 2020, pp. 1092–1094. doi: 10.1109/APMC47863.2020.9331463.
  15. F. Li, C. Wang, X. Zhang, F. Hu, W. Jia, and Y. Fan, “Features of Hierarchical Fuzzy Entropy of Stroke Based on EEG Signal and Its Application in Stroke Classification,” in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019, pp. 284–289. doi: 10.1109/BigDataService.2019.00050.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-08-30

Issue

Volume 9, Issue 4, July-August-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Pooja Mitra, Dr. Sheshang Degadwala, Dhairya Vyas, " Ensemble Classifier for Stroke Prediction with Recurshive Feature Elimination" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.357-364, July-August-2023. Available at doi : https://doi.org/10.32628/CSEIT2390430