Stroke Prediction Based on Machine Learning - Zhang Yuhan (ITM Web of Conferences)
Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM). Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. Analysis of the quantitative results indicates that the Random Forest model reaches an accuracy of 95% and a precision of 93%, providing a slight edge over the SVM, which records an accuracy of 92% and a precision of 90%.. However, both models exhibit high false-negative rates, with Random Forest showing a false-negative rate of 12% and SVM at 15%, which significantly impacts their clinical utility. To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. This study highlights the potential and limitations of machine learning in stroke prediction, showing that people need further optimization to enhance diagnostic performance.