Stroke Prediction Using Machine Learning Techniques

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@article{1710048,
      author = { Abubakar Abdulrauf, Dr Ibrahim sulaiman, Dr Danlami Gabi, Kabiru labaran Bala, Abdullahi?Bashar?Abubakar},
      title = {Stroke Prediction Using Machine Learning Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {2},
      pages = {670-675},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710048.pdf},
      abstract = {The challenge in stroke prediction stems from the complexity of risk factors associated with this condition, with traditional methods often overlooking the intricate interplay of physiological, lifestyle, and environmental factors. Various researchers have attempted to address this problem using logistic regression, decision trees, support vector machines, and neural networks, but these approaches face limitations such as handling class imbalance and capturing non-linear relationships among risk factors. This study aims to develop an advanced machine learning-based model for accurate stroke risk prediction by identifying comprehensive risk factors, collecting robust datasets, and comparing multiple algorithms including logistic regression, random forest, support vector machines, and neural networks. Evaluation results showed high overall accuracy (around 93.9%) across all models, though precision, recall, and F1-scores for stroke cases (class 1) were low. The proposed model improved performance slightly compared to traditional methods, particularly in handling class imbalance and complex relationships, providing a promising pathway for enhanced stroke prevention and patient care through data-driven predictive modeling style.},
      keywords = {Machine Learning, Stroke, SVM, RF and Data Set.},
      month = {August},
  }