Multimodal Approach for Predicting the Brain Stroke Using the Machine Learning Techniques
Manjula Gururaj Rao, P Karthik, Kishan R Kotian, Dhanush P Hegde
Abstract
Health is important for all living beings. The prevention of any disease is always better. Some of the diseases, like heart attack and stroke, can be prevented. The majority of strokes are caused by an unexpected blockage of pathways in the heart and brain. One of the deadliest disorders is brain stroke and now is a typical occurrence. The effects of a stroke could be fatality, severe disability, or permanent brain damage. Stroke requires immediate medical treatment. The severity of a stroke can be lessened by early recognition of numerous stroke warning symptoms. Due to this, a lower death rate can be achieved. In this research, the data collected from the patients/subjects is divided into the dependent variable and the non-dependent variable. These dependent and non-dependent variables are clustered and applied with the different machine learning algorithms such as Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Light GBM (LGBM) and XG Boost (XGB). Along with this, the model is developed to predict the best result. The model that was created is compared to all of the other models, and the results are discussed.