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@article{182390,
        author = {Ms Aqueela Bano and Mr Sarvesh Singh Rai},
        title = {Optimized Brain Stroke Diagnosis Prediction Using Random Forest and Bagging Classifiers: A Comparative Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1699-1705},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182390},
        abstract = {Stroke is a fault-finding healing condition needing swift and correct disease to underrate unending results. This dissertation introduces a progressive machine intelligence-located stroke disease plan leveraging the Random Forest Classifier and Bagging Classifier. Comprehensive preprocessing, including management absent principles, normative dossier, and addressing imbalances through methods like resampling, guarantees the dataset's condition. The Random Forest Classifier attained a train veracity of 100% and test accuracy of 99%, while the Bagging Classifier accomplished train and test accuracies of 99% and 98%, individually. By resolving key patient attributes in the way that age, hypertension, ischemic heart disease history, and oxygen level, and BMI, the models illustrated extraordinary predicting skill. This whole supports early and reliable stroke discovery, stressing model interpretability, moral concerns, and honest-world relevance in dispassionate backgrounds. The verdicts emphasize the transformative potential of machine intelligence in reinforcing stroke disease and patient care.},
        keywords = {Stroke Diagnosis, Random Forest Classifier, Bagging Classifier, Machine Learning, Data Preprocessing},
        month = {July},
        }