# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Classification Methodologies for Addressing Class Imbalance and Missing Data  
Addressing class imbalance and missing data in machine learning is crucial, particularly in medical applications such as stroke prediction. Imbalanced datasets can lead to models that disproportionately favor the majority class, resulting in poor predictive performance for the minority class, which is often the critical condition (e.g., stroke). In three comment articles, this issue was described as one that significantly influences the performance of the forecasts and models.  
The situation where the classes are imbalanced and missing values are in the data can be considered an issue as far as machine learning is concerned, particularly when it comes to medical applications. In recent years, the classification of imbalanced datasets has been one of the main problems for machine learning modeling. Imbalanced datasets with two classes mean one class dominates over another one. Thus, when a machine learning model is created that focuses on frequently privileged samples, it typically ignores less common but more critical samples. For example, in the case of stroke prediction, if the number of samples with stroke is significantly less compared to the number of samples without stroke, then the outcomes of a model cannot identify people at high risk.  
With the continuous extension of data availability in complex, large-scale, and networked systems, such as security, finance, and the Internet, it is essential to elevate the basic understanding of knowledge in this area. Even though existing techniques have revealed great success in many real-world applications, the issue of learning from imbalanced data is new. Therefore, this problem needs more engagement from researchers.  
Several survey articles related to the imbalanced learning area have been published in the past few years. In [16], the authors review SVM's weakness in handling imbalanced datasets and describe why there are better choices than the traditional under-sampling approaches. Another survey about class imbalance learning methods for SVM was collected in [17]. The purpose of the survey was to review different data preprocessing and algorithmic approaches to improve the performance of SVM in learning from imbalanced datasets. The critical issue in knowledge discovery and data engineering is imbalanced learning [18]. Their concentration was to present a challenging review of the nature of the issue, existing evaluation metrics, and state-of-the-art technologies utilized to assess learning performance under the imbalanced learning scenario. They used some significant assessments as a perfect reference for present and subsequent knowledge discoveries.  
Likewise, missing values in data are a disadvantage in model training and assessment. This situation occurs when missing or erroneous information that contributes to the formation of subsets of features is present in the learning samples. This is even more dangerous in contexts like medicine since risk scoring relies more on accurate information.  
Solving these problems is achieved through resampling to balance the classes and other complex imputation methods to handle missing values. By addressing these problems, the efficacy and efficiency of theoretical estimations can be significantly enhanced by applying them to medicine in diagnosis and patient care.