Paper Contents
Abstract
The interruption or reduction of the blood supply to the brain causes a stroke. A stroke is a condition where there is insufficient blood supply to the brain, which causes cell death. Today, it is the main cause of death in the entire world. Examining the affected individuals has shown a number of risk variables that are thought to be connected to the stroke's origin. Numerous studies have been conducted for the prediction and classification of stroke disorders using these risk variables. Machine learning and data mining methods are the foundation of the majority of the models. The stroke deprives the brain of oxygen and nutrients, perhaps leading to the death of brain cells. Numerous studies have been conducted to compare the effectiveness of predictive data mining methods in the prediction of various diseases. On the Cardiovascular Health Study dataset, we evaluate various approaches for predicting stroke with our algorithm in this article. Here, the principal component analysis algorithm is used to reduce the dimension, the decision tree algorithm is used to pick the features, and random forest algorithm is used to build a classification model. Our work offers the best predictive model for the stroke disease with 94.7% accuracy after analyzing and comparing classification efficiency with other approaches and variation models.
Copyright
Copyright © 2023 Divya K. This is an open access article distributed under the Creative Commons Attribution License.
