Application of Machine Learning Methods in Stroke Prediction

Abstract

Stroke is currently one of the largest causes of death and disability worldwide, hence it needs effective strategies to detect it early. The paper discusses the role that ML techniques may play in predicting stroke risk, along with methodologies, performance metrics, and their clinical implications. There are benefits associated with integrating ML into stroke prediction models: large dataset processing, improved accuracy, and timely risk assessment. In this paper, we have considered five distinct ML models: Linear Regression, Logistic Regression, Support Vector Machines, Random Forest, and Neural Networks. All these models provide different perspectives and insights about stroke risk analysis. In this work, vigorous analytical methods will be used that involve correlation analysis, SVM, and logistic regression. All these analyses will source a comprehensive stroke dataset from Kaggle. The results show that parameters like age, heart disease, glucose level, and hypertension are important indices that could predict the risk of stroke. Notwithstanding the odds of challenges that may result from data imbalance and bias, our study is going to implement various strategies aimed at mitigating these issues to provide precision and accuracy in our findings. We conclude from the study that, indeed, ML algorithms, especially deep neural networks, are capable of providing effective improvement in the prediction of long-term outcome of patients who have ever suffered from ischemic stroke. This current study joins other efforts in progress geared toward improving early detection and, consequently, treatment outcomes for stroke patients.