Leveraging AdaBoost and CatBoost to Classify the Likelihood of Brain Stroke
DOI:
https://doi.org/10.3329/jsr.v16i3.67891Abstract
Brain Stroke occurs when the blood flow to a portion of the brain is reduced or stopped, denying the brain's tissue nourishment and oxygen, which results in brain cell death. Many lives can be saved by early diagnosis, but the bulk of clinical datasets, including the stroke dataset, are unbalanced, which means that the majority of predictive algorithms are biased. By balancing the dataset, resampling methods improve machine learning algorithms' capacity for prediction. This study compares various algorithms on a stroke dataset to determine the likelihood of experiencing a stroke. In order to predict stroke, the authors of this work used two machine learning classifiers, AdaBoost and CatBoost, in conjunction with a well-known resampling technique called Synthetic Minority Oversampling Technique (SMOTE). A publicly available dataset was employed for the study. CatBoost outperformed AdaBoost and achieved an accuracy of 96 % when combined with SMOTE. The accuracy achieved using CatBoost was better than that of most previously developed models and is on par with other advanced models.
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Articles published in the "Journal of Scientific Research" are Open Access articles under a Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0). This license permits use, distribution and reproduction in any medium, provided the original work is properly cited and initial publication in this journal. In addition to that, users must provide a link to the license, indicate if changes are made and distribute using the same license as original if the original content has been remixed, transformed or built upon.