Leveraging Adaboost and Gridsearchcv for Optimized Stroke Prediction Outcomes
| dc.contributor.author | Babatunde, R.S., Babatunde, A.N., Abdulsalam, S.O. Oludare, R.O., Abdulsalam, M.K., Olabode, J.O. | |
| dc.date.accessioned | 2025-10-18T18:40:23Z | |
| dc.date.available | 2025-10-18T18:40:23Z | |
| dc.date.issued | 2025-04-08 | |
| dc.description.abstract | Background: Globally, stroke is one of the most common causes of mortality and long-term disability. According to World Health Organization (WHO) report, 15 million people suffer a stroke annually, with 5 million dying and another 5 million becoming permanently disabled, placing a major burden on families and communities. Some researchers have attempted to predict stroke using machine learning algorithms; however, while these algorithms yielded high accuracy, limited attention was given to hyperparameter tuning and class imbalance issues. Aim: This study addresses the critical need for enhanced stroke prediction by employing the AdaBoost machine learning algorithm, optimized through GridSearchCV for hyperparameter tuning to mitigate the aforementioned problems. Method: Utilizing a dataset from Kaggle, pre-processing steps, including feature selection, encoding, and class balancing via synthetic minority oversampling technique (SMOTE), were implemented. The model's performance significantly improved post-tuning, with accuracy rising from 75.42% to 84.33%, precision from 71% to 79%, and recall from 87% to 93%. These results highlight the importance of hyperparameter optimization in enhancing machine learning models for medical diagnostics. Results: The findings suggest that the optimized AdaBoost model can aid in early stroke detection, potentially improving patient outcomes and offering a valuable tool for healthcare providers. This study contributes to the field by setting a benchmark for stroke prediction and providing a foundation for future advancements in predictive analytics. Keywords: prediction, adaboost, gridsearchcv, machine learning, hyperparameter tuning | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/5929 | |
| dc.publisher | Sule Lamido University Journal of Science and Technology | |
| dc.title | Leveraging Adaboost and Gridsearchcv for Optimized Stroke Prediction Outcomes |