<![CDATA[Coronary Heart Disease (CHD) is a leading cardiovascular disease and one of the primary causes of death worldwide. Early and accurate classification of CHD can aid in effective prevention and appropriate treatment. This study aims to develop a CHD classification model using the Extreme Learning Machine (ELM) method. The research methodology includes gathering CHD data from the Cleveland Heart Disease Dataset, data preprocessing, dividing data into training and testing sets, and implementing the ELM algorithm for classification. Additionally, a literature review was conducted to identify related studies on heart disease classification using machine learning methods. The results indicate that the ELM model can classify CHD effectively and efficiently compared to other methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). Therefore, ELM presents a promising alternative for early CHD diagnosis.]]>
Copyrights © 2024
