# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 4 Discussion

Table 6: Best performance results: Evaluation of classifiers using mean imputation and feature importance by [PERSON] et al. [5]. Ref. [4] put forward another method, which we refer to as Methodology B, which is majorly centered on the issues of missing data and class imbalance. Realizing that missing values could significantly affect the quality of the predictive models, ref. [4] used the following imputations: mean imputation, Multiple Imputation using Chained Equation (MICE), and age group imputation. The MICE technique, most especially, utilizes regression equations in imputing the missing values while maintaining the integrity of the dataset. When dealing with class imbalance, the present study used the Synthetic Minority Oversampling Technique (SMOTE), which creates new samples of the minority class to maintain the dataset balance. Nevertheless, [PERSON] et al. cited the identical drawback, which is overfitting when utilizing SMOTE, and pointed to the necessity of setting up the parameters of a method. The Dense Stacking Ensemble (DSE) model, which integrates multiple classifiers, yielded an accuracy of over 96 percent. This is the usual procedure of compiling the votes of various models. As demonstrated here, it effectively captures the different characteristics of a complex and imbalanced dataset. In a follow-up, ref. [5] presented Methodology C, where feature selection and multiple classifiers were used to improve stroke prediction. SMOTE was used to correct class imbalance. Data were cleaned by performing mean imputation for missing data, where missing data values were replaced by the mean value of that feature to make the dataset complete and ready for analysis. Methodology C employs three feature selection techniques. The valuable metrics used are Mutual Information (MI), Pearson's Correlation (PC), and Feature Importance (FI). MI determines the dependency between the features and target variable strength, while PC captures the dependency between the two features. Based on the Random Forest algorithm, FI ranks the features in terms of their relevance to the model. These techniques were instrumental in identifying the most relevant features, which were then used in combination with five different classifiers. These came top from the best-performing algorithms for classification, namely, the Naive Bayes, Support Vector Machine, Random Forest, Adaptive Boosting, and Extreme Gradient Boosting. Ten-fold cross-validation was used to determine the performance of these classifiers by dividing the dataset into ten sets. Nine subclasses were taken for training, the last for testing, and the whole process was repeated ten times. The Random Forest classifier with FI selection yielded an accuracy of 97.17%, which is what has been illustrated earlier: using feature selection techniques accompanied by class balancing yielded the best results. It was rich in offering a detailed outlook on the methods employed to enhance the model's accuracy concerning stroke prediction; this included an appreciation of how the features needed to be selected. The subsequent comparison of these methodologies provides several lessons regarding stroke prediction. Among the three mentioned studies, [PERSON] et al. 's approach--since it emphasizes deep learning and data preprocessing--resulted in a comparably higher accuracy, implying that their proposed method might be most suited for highly accurate contexts. Their use of synthetic data generation and feature transformation played a crucial role in overcoming data imbalance and enhancing model performance. Compared to the DSE model, the methodology of [4] is slightly less accurate, but it has several advantages. The first one is that it effectively works with missing data and helps to avoid overfitting, which is implemented in the SMOTE algorithm. In addition, stuck in numerous classifiers, the DSE model can select the most suitable one based on specific characteristics of the given dataset. For this reason, [PERSON] et al.