Multi-Layer Perceptron For Diagnosing Stroke With The SMOTE Method In Overcoming Data Imbalances

Abstract

Stroke is the sudden loss of brain function due to an interruption of the blood supply to the brain. Stroke is a dangerous disease that can even cause death for patients. The diagnosis of stroke must be made quickly and precisely to increase the likelihood that the patient can live a normal life again. In making a diagnosis, several factors can influence the patient to get a stroke diagnosis, including symptoms of hypertension to heart disease. From these problems, the researcher wants to classify the diagnosis of stroke so that stroke can get earlier treatment so that patients do not experience prolonged illness. The data used in this study is a stroke dataset with 4861 data labeled 0 which indicates no stroke, and 249 data labeled 1 which indicates a stroke diagnosis. This study uses the Synthetic Minority Over-sampling (SMOTE) method that will be applied to the Multi-Layer Perceptron algorithm so that researchers can get the performance of the stroke diagnosis classification model. Researchers use the SMOTE method so that the data in the classification model is balanced so that the model can make accurate predictions and avoid overfitting on the Multi-Layer Perceptron so that the accuracy in predicting stroke is better than just using an ordinary Multi-Layer Perceptron. The results of the confusion matrix analysis show that SMOTE can increase the prediction of stroke diagnosis from 12,5% to 84,89% in optimal test.