Optimization of Genetic Algorithm from Comparison of Machine Learning for Heart Disease Prediction - Purwati, Neni; Nurlistiani, Rini (Jurusan Informatika Universitas Tanjungpura, 2025)
Ischemic Heart Disease (IHD) is the leading cause of death worldwide, accounting for 13% of global fatalities. The number of deaths caused by IHD rose from 2.7 million in 2000 to 9.1 million in 2021, an increase of 6.4 million. IHD can be diagnosed through medical examinations or various health tests, as well as by leveraging technological advancements in artificial intelligence to enable early disease detection. This early detection is crucial for preventing heart disease, as there is currently no cure for the condition. This study aims to compare machine learning algorithms based on decision tree methods (Decision Tree, Random Forest, and Gradient Boosted Tree) with optimization using genetic algorithms to predict heart disease. The dataset used includes information from 8,625 patients who have experienced heart attacks, featuring attributes such as Sex, General Health, Age Category, Height (in meters), Weight (in kilograms), BMI, and