Brain Stroke Prediction Model Based SMOTE and Machine Learning Algorithms

A brain stroke is a critical medical emergency condition that causes disability and death. The pre-diagnosis of this case can reduce the complications and problems that affect the brain as a result of being affected by the complications that occur during the injury. This study lists an analysis process on a brain stroke dataset using the KNIME tool, which provides a set of different machine learning components such as random forest, Decision Tree Learner, Gradient Boosted Trees Learner, and Logistic Regression algorithms. The problem of imbalanced data will be handled as part of data preprocessing. The factors that affect the brain stroke will be explored based on feature selection approaches such as forward feature selection, backward feature elimination, genetic algorithms, and random. The aim is to build a model that helps doctors diagnose the disease accurately based on the results we obtained from the study and analysis. The results showed that logistic regression outperformed the other algorithms after applying the algorithm with forward feature selection and backward feature elimination