# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Application of the Methodology
#### 2.4.2 Missing Values  
Handling missing values is critical to the effectiveness of machine learning, and the choice between imputation and imputation depends on the particular characteristics of the dataset and the number of missing values. The variable \"BMI\" (Table 1) contained 201 missing values out of 5110 observations, representing about 3.93% of the data (Figure 1). The authors' approaches to handling missing values in the three studies demonstrate a diversity in methodology and strategies for dealing with incomplete data.  
The authors considered several main strategies and conducted experiments to deal with missing values: exclusion of cases with missing information or using mean, median, and mode for average values. Subsequently, they also excluded the observations where they observed missing values since the number of instances was relatively lesser; using imputation would skew the results. Further, the authors dropped the \"id\" column (Table 1) as this column only has numerical values for the unique identification of patients, and such columns do not include information relevant to the risk of stroke. Due to all these steps, the data frame was reduced to 4909 rows and 11 columns, and then used for analysis and modeling.  
[PERSON] et al. detailed experiments using mean imputation and data removal methods in their article. However, data removal delivered superior results to imputation, so they incorporated it into their Methodology A.  
Figure 1: Sparsity matrix for the dataset.  
Imputation Methods to Address Missing Values--Methodology B  
The authors ([PERSON] et al.) of Methodology B chose to use imputation techniques instead of removing rows with missing values to avoid losing data and insights. They carefully chose imputation techniques to mitigate the risk of introducing assumptions or biases. The effectiveness of these imputation methods was evaluated in comparison to the traditional approach of listwise deletion, aiming to balance data utility with accuracy and minimize any potential bias introduced by the imputation process. Their study employed three distinct imputation methods to address missing values effectively. Mean imputation: this imputation technique fills in missing values about the mean of the variable value; this methodology does not negatively affect data integrity and analysis arising from incomplete data. It permits the maximum information available, resulting in better and more stable results. Multivariate Imputation by Chained Equations (MICE): multiple imputations and sequential regression together in MICE are superior to filling in missing variables more accurately. This approach improves imputation accuracy and reliability by incorporating data from other variables. Imputation of BMI based on age group: when BMI was not reported, the mean BMI of patients in their given age range (0-20, 21-40, 41-60, 61-80) was considered. This accounted for the difference between age and strategy, which helped analyze the specified research to adopt a less general approach toward the relation between age and BMI.  
Imputation Methods for Addressing Missing Values--Methodology C  
The authors ([PERSON] and co-authors) used the mean imputation method to fill in the missing values in their research. They chose this approach as part of their methodology to maintain the integrity of the data and ensure more accurate results in the analysis in the context of their specific research objectives and approach.