Machine Learning based Brain Stroke Prediction using Light Gradient Boosting Machine Algorithm
Keywords:
Brain stroke prediction, machine learning, Logistic Regression, K-Nearest Neighbor, Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine.Abstract
Timely detection and proactive measures to prevent stroke are of highest priority due to the effective likelihood of extreme disabilities or destructive effects associated with this disease. Stroke diseases can be separated into two categories those are ischemic stroke and Hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. The Early detection of the multiple stroke warning symptoms can facilitate the stroke's harshness. The main purpose of this study is to predict the possibility of a brain stroke happening at an early stage using machine learning algorithms. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. Various classification models, including, K-Nearest Neighbor (K-NN), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were successfully utilized in this analysis for classification studies. The performance of the methodology is evaluated using Precision, Recall, and F1-Score evaluation metrics. With experimental results, we can show that the proposed LightGBM classifier has 99% classification accuracy, which was the highest (among the machine learning classifiers).
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