Optimizing Stroke Risk Prediction Using XGBoost and Deep Neural Networks

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.

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