# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
###### Abstract
In contrast, a study [4] by [PERSON] et al. utilized multiple imputation techniques for handling missing data, with MICE (Multiple Imputation by Chained Equations) offering the best results. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The Dense Stacking Ensemble (DSE) model employed by [PERSON] et al. showed exceptional performance, particularly on balanced datasets, affirming the efficacy of their chosen imputation and ensemble learning techniques. A study [5] by [PERSON] et al. combined multiple feature selection processes and classifiers. They used SMOTE for class balancing and examined various classifiers, ultimately identifying Random Forest with Feature Importance as the most effective, achieving an accuracy of 97.17%. This study demonstrated the significant impact of feature selection and class balancing on model performance. The published studies also complement the issues of data imbalance and missing data by drawing attention to the selection of features and the tuning of algorithms. They reveal that with the right choice of features and efficient algorithms, researchers can construct models that predict strokes more efficiently and give an idea about how to prevent strokes or treat the affected individual. Based on the comparison of the findings from the reviewed studies, it was found that there is a diversification of methodologies and research studies that all contribute to a better understanding of stroke prediction. [PERSON] et al. emphasize SVM and the implementation of resampling techniques; [PERSON] et al. 's application of SMOTE and ensemble, and [PERSON] et al. 's hybrid system development, present the field's current and most potentially developing scenario. Each approach is advantageous and disadvantageous, highlighting the complications in predicting a stroke and emphasizing the requirement for further advancements in the topic. Incorporating machine learning in stroke prediction is one step in improving modern healthcare analytics. Since dealing with imbalanced data, missing data, and feature selection are critical, they call for effective solutions and proper assessment to improve accurate models. This article sets the stage for a detailed exploration of the methodologies and results presented in the reviewed studies, offering a critical evaluation of their contributions and identifying best practices for future research. The comparative analysis reveals that while each study presents unique methodologies and strengths, a critical takeaway is the importance of tailoring model selection and techniques to the specific characteristics of the dataset. This research demonstrates the valuable contributions of AI and machine learning to stroke prediction, highlighting the necessity for ongoing advancements to enhance predictive accuracy and, ultimately, improve patient outcomes. In conclusion, predicting strokes using machine learning offers substantial early diagnosis and prevention opportunities. This research synthesized methodologies and findings from various studies, providing insights into best practices and areas for further improvement. Integrating effective data handling techniques and model optimization remains crucial to advancing stroke prediction models and positively impacting global health outcomes. We highlight a paper [6], [PERSON] et al., 2024, in which the authors systematically review articles published up to August 2023 with the subject of stroke prediction.