Comparison of Bagging and Adaboost Methods on C4.5 Algorithm for Stroke Prediction

Abstract

cerebrovascular disease secara cepat dapat menyebabkan kematian. Tujuan dari penelitian ini adalah untuk mengatasi masalah tersebut adalah membuat model prediksi berbasis machine learning untuk membantu ahli medis menangani penyakit stroke untuk mengurangi risiko kematian. Metode yang diterapkan untuk penelitian ini adalah menerapkan metode klasifikasi algoritma C4.5 serta metode bagging dan Adaboost dari Ensemble Learning . Data stroke diolah menggunakan 2 Abstract Stroke is a non-communicable disease and is very dangerous because of functional disorders of the brain caused by blockage of blood circulation. This disease is classified as a cerebrovascular disease because it requires treatment for 24 hours, if not treated quickly it can cause death. The purpose of this research is to overcome this problem is to create a machine learning-based prediction model for medical experts in dealing with diseases to help reduce the risk of death. The method applied for this research is to apply the C4.5 algorithm classification method as well as the bagging and Adaboost methods from Ensemble Learning. Stroke data is processed using 2 stages of data processing, namely the data cleaning