# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
###### Abstract  
Stroke prediction is a vital research area due to its significant implications for public health. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. [PERSON] et al. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% accuracy and a 97% recall rate. They utilized resampling methods to balance the classes and advanced imputation techniques to handle missing data, underscoring the critical role of data preprocessing in enhancing the performance of Support Vector Machines (SVMs). [PERSON] et al. addressed missing data and class imbalance using multiple imputations and the Synthetic Minority Oversampling Technique (SMOTE). They developed a Dense Stacking Ensemble (DESI) model with over 96% accuracy. Their results underscore the efficiency of ensemble learning techniques and imputation for handling imbalanced datasets in stroke prediction. [PERSON] et al. employed various classifiers and feature selection techniques, including SMOTE, for class balancing. Their Random Forest (RF) classifier, combined with Feature Importance (FI) selection, achieved an accuracy of 97.17%, illustrating the positive impact of RF and relevant feature selection on model performance. A comparative analysis indicated that [PERSON] et al. 's method achieved the highest accuracy rate. However, the studies collectively highlight that the choice of models and techniques for stroke prediction should be tailored to the specific characteristics of the dataset used. This study emphasizes the importance of effective data management and model selection in enhancing predictive performance. stroke prediction; machine learning modeling; classification models; imbalanced dataset +
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stroke prognostication, utilizing datasets available on the Kaggle platform. These studies consider various demographic, medical history, lifestyle, and physiological features crucial for predicting stroke. However, shared challenges such as missing data and class imbalance in the observations can impede predictive accuracy and need addressing. This article reviews three significant studies by [PERSON] et al., [PERSON] et al., and [PERSON] et al., all published in the Scopus database post-August 2023. The comparison focuses on standard classification metrics such as accuracy, precision, sensitivity, and specificity, with each selected methodology demonstrating evaluation parameters above 95%. The benchmarking process identifies essential steps in developing effective algorithmic methodologies, including handling missing data, managing unbalanced datasets, selecting appropriate classification models and parameters, and assessing classification results. As a result, the methodology creates an innovative solution implemented through a machine learning model. One of the main issues with stroke prediction datasets is class imbalance, where 95% of records belong to one class, and only 5% belong to the stroke class. Addressing this imbalance is crucial to avoiding model biases that favor the majority class, which can lead to suboptimal performance in predicting the minority class. Practical solutions to these challenges are vital for developing accurate and reliable stroke risk prediction models. A study [3] by [PERSON] et al. emphasized addressing imbalanced data and missing values through advanced machine learning techniques and optimization models. They applied Support Vector Machines (SVMs), Decision Trees (DTs), and Random Forests (RFs) using class balancing techniques and robust imputation methods. The SVM model achieved the highest accuracy (98%) and recall with specific parameter configurations, demonstrating superior predictive capability. Their Random Forest and decision tree models also performed well, highlighting the importance of model selection and configuration.