# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility
### Procedure and Experimental Methodology
#### 2.3.3 Missing Values  
Handling missing values is an important aspect of data preprocessing in machine learning. In our dataset, we encountered missing values in the variable 'bmi', which is a common occurrence in real-world datasets. After running a check for missing values, we found that 201 observations had missing values for the 'bmi' variable.  
To deal with missing values, we had two options--either we could remove the observations with missing values or impute the missing values. In our case, we chose to remove the observations with missing values since the number of missing values was relatively small, and imputing the values could have introduced bias in the dataset.  
We also noticed that the 'id' column only contains the patient's unique identifier and is not relevant to the prediction of stroke. Therefore, we dropped this column from the dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Description** \\ \hline \multirow{3}{*}{Gender} & Some studies have shown that men have a higher risk of stroke than women. However, gender alone is not a strong predictor of stroke risk. \\ \hline \multirow{3}{*}{Age} & Age is a significant risk factor for stroke, as the risk of stroke increases with age. The risk of stroke doubles \\  & every decade after the age of 55. \\ \hline \multirow{3}{*}{Hypertension} & Hypertension, also known as high blood pressure, is a significant risk factor for stroke. Individuals with hypertension have an increased risk of stroke compared to those without hypertension. \\ \hline \multirow{3}{*}{Heart disease} & Heart disease is another significant risk factor for stroke. \\  & Individuals with heart disease are at a higher risk of stroke compared to those without heart disease. \\ \hline \multirow{3}{*}{Ever married} & Whether or not a person is married does not have a direct relationship with stroke risk. \\ \hline \multirow{3}{*}{Work Type} & Work type may indirectly impact stroke risk by influencing lifestyle factors such as physical activity, stress, and diet. \\ \hline \multirow{3}{*}{Residence Type} & Residence type may indirectly impact stroke risk by influencing lifestyle factors such as access to healthy food, air pollution, and physical activity opportunities. \\ \hline \multirow{3}{*}{Average Glucose Level} & High blood sugar levels can increase the risk of stroke, particularly in individuals with diabetes. \\ \hline \multirow{3}{*}{Body Mass Index BMI} & Obesity is a significant risk factor for stroke. A high BMI \\  & indicates a higher risk of stroke. \\ \hline \multirow{3}{*}{Smoking Status} & Smoking is a significant risk factor for stroke. \\  & Individuals who smoke have a higher risk of stroke compared to non-smokers. \\ \hline \multirow{3}{*}{Stroke} & This variable indicates whether or not the patient has experienced a stroke. It is the dependent variable in the dataset and is predicted by the other variables in the dataset. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the dataset.  
After removing the observations with missing values and dropping the 'id' column, the dataset was left with 4909 rows and 11 columns. This dataset was used for further analysis and model building.  
It is important to note that handling missing values can have a significant impact on the performance of machine learning models. In some cases, inputting missing values may be a better approach than removing them, especially if the number of missing values is large. Therefore, the choice of handling missing values depends on the specific problem and the characteristics of the dataset.  
In summary, handling missing values is an important step in data preprocessing, and in our case, we chose to remove the observations with missing values and drop the irrelevant 'id' column. The resulting dataset had 4909 rows and 11 columns, which were used for further analysis and model building.