Predicting Stroke Risk with Machine Learning and Hyperparameter Optimization
Abstract
Stroke is a serious medical condition that causes the death of brain cells due to insufficient blood flow due to blockage or rupture in the blood vessels leading to the brain. Stroke is the most common cause of death and disability in adults after heart attack and cancer, causing individuals to not only die but also live with permanent disabilities. In this study, 12 features and 7 different machine learning methods belonging to 5100 individuals in an open-source dataset were used to predict stroke risk. Hyperparameter optimization was applied to increase the performance of machine learning methods and the best parameters were selected. When the results were examined, the random forest algorithm was able to detect the risk of stroke with an accuracy of 96.98%, which is higher than other studies in literature. This study discusses the effective use of machine learning algorithms to predict stroke risk and efforts to improve model performance. The results obtained may help in more accurate determination of stroke risk and taking preventive measures.
Keywords
Classification, Hyperparameter optimization, Stroke, Machine learning
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