A Prediction Model for Stroke Based on Machine Learning Algorithms

In this modern era, people are working hard to meet their physical needs and non-effective their ability to spend time for themselves which leads to physical stress and mental disorder. Many reports state that stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. In this research work, with the aid of machine learning (ML), several algorithms has been used and evaluated for the long term risk prediction of stroke occurrence. We have collected datasets to analyze data and mining using 8 algorithms of machine learning to predict whether the patient suffers from stroke or not. This paper used a dataset retrieved from kaggle repository, which consists of 12 attributes (Features). This work is implemented using K-Nearest Neighbors (KNN), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (TD), Neural Network (NN) and eXtreme Gradient Boosting (XGB) algorithms. Results showed eXtreme Gradient Boosting (XGB) gave the best result with an accuracy of up to 95.14%.