Classification model for type of stroke using kernel logistic regression

Abstract

Stroke is the second leading cause of death in the world and has a high contribution to disability. Many stroke sufferers do not recognize the symptoms of stroke or do not even have knowledge related to stroke. This causes many sufferers to be late to the hospital for first aid. This can lead to even greater risk. One effort that can be done is to find out the factors that influence stroke so that it can be prevented. This study aims to create a classification model that can be used to predict the type of stroke and to find out what factors have a significant effect on the type of stroke. The method used is Kernel Logistic Regression (KLR) which is the development of Logistic Regression (LR) by using a linear combination of regularized LR. In the modeling, two scenarios for the distribution of training and testing data were also carried out, namely, scenarios 7:3 and 8:2. The results of the accuracy of the two scenarios, are 75.97% for scenario 8:2 and 73.97% for scenario 7:3. The accuracy of the KLR is 92.12% which increased by 16.15% from the LR. From the modeling results for scenario 8:2, it was found that four predictors affected the type of stroke significantly, namely cholesterol level, temperature, length of stay, and disease history.