Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm

Abd Mizwar A Rahim; Andi Sunyoto; Muhammad Rudyanto Arief

doi:10.30812/matrik.v21i3.1666

Abd Mizwar A Rahim Universitas Amikom Yogyakarta
Andi Sunyoto Universitas Amikom Yogyakarta
Muhammad Rudyanto Arief Universitas Amikom Yogyakarta

DOI: https://doi.org/10.30812/matrik.v21i3.1666

Keywords: Cardiovascular Disease, Ensemble Learning, Machine Learning, Stroke Prediction, Xtreme Gradient Boosting

Abstract

Based on data obtained from WHO, stroke is a disease that ranks as the second most deadly disease. The cause of a stroke is when a blood vessel is hit or ruptured, resulting in a part of the brain not getting the blood supply that carries the oxygen it needs, leading to death. By utilizing technology in the health sciences, especially in the health sector, machine learning models can adjust and make it easier for users to predict certain diseases. Previous studies have had problems with low accuracy when used in healthcare. The purpose of this research is to increase accuracy by proposing the application of one of the ensemble learning algorithms, namely the Xtreme Gradient Boosting algorithm. This stroke prediction research uses the Xtreme Gradient Boosting Algorithm; the application of this method with split data Training data and 70/30 test data, 70% of the training data is 3582, 30% of the test data is 1536, and the results are 96% accuracy with these results having good results. This study increase accuracy in predicting stroke cases and get better accuracy than previous studies.

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