IMPLEMENTATION OF SUPPORT VECTOR MACHINE ALGORITHM WITH HYPER-TUNING RANDOMIZED SEARCH IN STROKE PREDICTION

Abstract

Stroke is a severe health problem and can significantly impact a person's quality of life. Therefore, it is crucial to predict stroke early so that preventive measures can be taken before it is too late. This study demonstrates the importance of hyper tuning and hyperparameters in a stroke prediction model. Literature studies show that many studies on stroke prediction need to explain this, even though this is very important for developing the performance of stroke prediction models. In this study, we use the Support Vector Machine (SVM) algorithm to predict stroke and evaluate the algorithm's performance without hyper tuning and with hyper tuning Randomized Search CV. We also divide the data into training and test data by 75% and 25%. The results of this study indicate that hyper-tuning can improve the accuracy of the stroke prediction algorithm. The algorithm's accuracy is 77% without hyper-tuning, whereas, with hyper-tuning, the accuracy increases to 96%. Hypertuning with the Randomized Search CV method can improve the performance of the stroke prediction algorithm and is very important to do in developing predictive models.