# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility
### Classification Methodology  
In recent years, imbalanced data have been a challenge in machine learning, particularly in the medical field. The class imbalance problem arises when the number of instances in one class significantly outweighs the number of instances in the other class, leading to biased learning and poor performance. To tackle this challenge in stroke prediction, we apply efficient machine learning models and a special data-oriented approach for dealing with imbalanced data. In the beginning, we formulate an optimization model as follows:  
\(\max\text{Acc}(X_{test})\)  
under conditions  
1. Construct the split of the given dataset in subsets \(X_{train}\) and \(X_{test}\), corresponding to the training and testing subsets. 2. Define the classification model. Choose the parameters for the model. 3. Fit the model on the training subset. 4. Evaluate the model on the test set. We propose an effective methodology to solve the proposed model. We describe how to realize each condition of the model. Our methodology involves data cleaning and preparation to ensure that the data are suitable for analysis. We deal with missing values by imputing them with the mean or median of the respective feature. Resampling data is a critical step in our methodology, and we use different approaches to apply the resampling method, including random undersampling for the majority class and random oversampling for the minority class. To create our model, we apply several machine learning models, including the random forest classifier, decision tree, and support vector machines. These models are well-suited for imbalanced datasets and can handle large amounts of data, leading to more accurate predictions. To address the class imbalance problem, we use a train-test split approach, separating 40% of observations from the majority class. We then increase the minority class using the resample command, with parameters such as \"replace = True\" and \"n_samples = constant\". The value of the constant is specific for each model and is determined through experimentation and optimization. Our methodology represents an algorithm that realizes the methodology to solve learning inhibitors within imbalanced datasets, providing a reliable and accurate prediction of stroke risk. By applying these contemporary models and methods, we hope to contribute to the growing body of research on the intersection of AI and public healthcare, leading to improved stroke prevention and treatment strategies. Stroke prediction is a critical task in public healthcare, and imbalanced data are a common challenge faced by researchers in this field. In this study, we tackled this chal lenge by applying contemporary models and methods to improve the prediction of stroke occurrence. Our methodology involved several steps, including data cleaning, preparation, dealing with missing values, and resampling [25]. We utilized random undersampling for the majority class and random oversampling for the minority class. To ensure the accuracy of our model, we separated 40% of observations from the majority class using the command train_test_split (..., test_size = 0.4, random_state = 86). Next, we increased the minority class using the resample command with parameters replace = True, n_samples = constant. The value of the constant was specific to each model used, which included a random forest classifier, decision tree, and support vector machines. Our approach was successful in improving the accuracy of stroke prediction compared to previous research efforts. By utilizing contemporary models and methods for dealing with imbalanced data, we were able to overcome the challenges of working with a skewed dataset and produce more reliable results. Moreover, our study contributes to the field of public healthcare by highlighting the importance of using advanced machine learning techniques to predict the occurrence of stroke.