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DOI: 10.14569/IJACSA.2024.0151114
PDF

Optimizing Stroke Risk Prediction Using XGBoost and Deep Neural Networks

Author 1: Renuka Agrawal
Author 2: Aaditya Ahire
Author 3: Dimple Mehta
Author 4: Preeti Hemnani
Author 5: Safa Hamdare

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 11, 2024.

  • Abstract and Keywords
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Abstract: Predicting brain strokes is inherently complex due to the multifaceted nature of brain health. Recent advancements in machine learning (ML) and deep learning (DL) algorithms have shown promise in forecasting stroke occurrences to a certain extent. This research paper explores the predictive potential of ML and DL models by utilizing a comprehensive dataset encom-passing diverse patient characteristics, including demographic factors, work culture, stress levels, lifestyle, and family history. Notably, this study incorporates 14 clinically significant attributes for prediction, surpassing the 10 attributes utilized by earlier researchers. To address existing limitations and enhance predictive accuracy, a novel ensemble model combining Deep Neural Networks (DNN) and Extreme Gradient Boosting (XGBoost) is proposed in this work. Also, a comparative analysis against individual DNN and XGBoost models, as well as Random Forest and Support Vector Machine (SVM) approaches are being done. The performance of the ensemble model is assessed using various metrics, including accuracy, precision, F1 score, and recall. The findings indicate that the DNN-XGBoost model exhibits superior predictive accuracy compared to standalone DNN and XGBoost models in identifying brain stroke occurrences.

Keywords: DNN; XGBoost; stress level; stroke prediction

Renuka Agrawal, Aaditya Ahire, Dimple Mehta, Preeti Hemnani and Safa Hamdare, “Optimizing Stroke Risk Prediction Using XGBoost and Deep Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151114

@article{Agrawal2024,
title = {Optimizing Stroke Risk Prediction Using XGBoost and Deep Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151114},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151114},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Renuka Agrawal and Aaditya Ahire and Dimple Mehta and Preeti Hemnani and Safa Hamdare}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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