Stroke Prediction Using Machine Learning

Abstract

Stroke, a cerebrovascular disease, stands as a prominent contributor to global mortality, imposing substantial health and financial challenges on individuals and healthcare systems alike. Notably, health-related behaviour has emerged as a crucial determinant of stroke risk, gaining increased attention in preventive efforts. Various machine learning models have been devised to forecast stroke risk or facilitate automated stroke diagnosis, leveraging predictors such as lifestyle elements and radiological imaging. Surprisingly, there is a notable absence of models utilizing data from laboratory tests. As the second leading cause of death worldwide, stroke persists as a significant health burden. This project employs machine learning principles on extensive existing datasets to predict stroke risk based on potentially modifiable factors. The subsequent goal involves developing an application to deliver personalized warnings according to individual stroke risk levels, accompanied by lifestyle correction messages addressing specific stroke risk factors. Stroke, a significant health concern globally, requires timely identification and intervention to prevent severe consequences. Preventing strokes involves promoting health and raising awareness of risk factors.