# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 1 Introduction
The main goal of the ML methods' implementation is classifying a potential stroke as correctly and timely as possible. Machine learning (ML) is a method for data analysis with the purpose of automatically creating an analytical model. ML revolves around the idea that systems can learn from available data, identify certain behaviors and patterns, and make decisions [12]. In order for Machine Learning models to learn from certain data, an algorithm has to be trained. Since the accuracy of the learning and training algorithm is the variable for the output of the research, people using Machine Learning methods to solve modern problems must aim for high model accuracy in order to be useful. What makes the task of creating an accurate ML model really hard is that in real life, people work with imbalanced data. There are different definitions for imbalanced datasets. From a practical point of view, we can indicate the website [13] to define the imbalanced data. The data are considered imbalanced when one of the data classes represents a tremendous proportion of the observations while the other data class forms a very small proportion (which is also the case for the research's dataset). For instance, a mild degree of imbalance is considered when the proportion of the minority class is roughly \"20-40% of the dataset\" [13], whereas an extreme imbalance is present when the minority class takes up less than \"1% of the dataset\" [13]. Data disproportion might become a problem when training a certain model, because the model itself would spend most of its time analyzing samples from one of the classes and not enough time analyzing samples from the other class(es), which would essentially result in an output model that can recognize new samples from the first class very well, but classify samples from the second class poorly, leading to major algorithmic bias. When applying Machine Learning to resolve a problem of this scope, one should use specific algorithms to deal with the uneven distribution. When it comes to predicting stroke within medical datasets, there are several machine learning algorithms that can be employed, including random forest models, decision tree models, and support vector machines (SVMs). Random forest models are a type of ensemble learning method that combines multiple decision trees to improve the accuracy of predictions. Each decision tree is built using a subset of the data, and the final prediction is determined by aggregating the results of each individual tree [14]. Decision tree models, on the other hand, are a type of supervised learning algorithm that works by recursively splitting the data into smaller subsets based on the values of specific variables. This process continues until a decision is made or a stopping criterion is met [15]. SVMs are a type of supervised learning algorithm that seeks to find the hyperplane that best separates the observations into different classes. SVMs are highly effective at handling high-dimensional data and can handle both linear and nonlinear relationships between variables. However, they can be computationally expensive and may be sensitive to the choice of kernel function, especially with very large volumes of observations [16]. In terms of predicting stroke within medical datasets, all three algorithms have been shown to be effective. For example, a study published in [17] found that a random forest model achieved an accuracy of 94% in predicting stroke risk based on medical data, and an accuracy of 94% within the decision tree implementation. SVMs have also been used successfully in predicting stroke risk based on medical data, with one study achieving an accuracy of 80% [18]. In the previous work of [PERSON] and [PERSON] [19], in order to predict the possibility of stroke, logistic regression, decision tree, and random forest methods were implemented. The most successful algorithm that was used was the random forest one--with 96% accuracy and a recall score of 0.966.