COMPARATIVE PERFORMANCE ANALYSIS OF MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORK MODELS FOR HEART DISEASE FORECASTING
Keywords:
cardiovascular disease prediction; machine learning; Random Forest; XGBoost; Support Vector Machine; artificial neural network; clinical classification.Abstract
Cardiovascular disease (CVD) is still the most common cause of death in the whole world. Identification of patients at risk on early stage is very important for making the decisions better clinically. In this study, four supervised machine learning models are used on the Cleveland cardiac Disease dataset. These models are Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and an Artificial Neural Network (ANN).
All models were trained using same dataset split. Where 80% data was used for training and 20% for testing. The dataset consists of 303 patient records with 13 clinical and demographic features. Before training, the data was normalized by using z-score standardization.
The results show that the Random Forest (RF) and XGBoost performed the best. Both achieving an accuracy of 98.53%. ANN achieves 94.14% and SVM achieves 88.78% with its default settings. Additional evaluation such as (ROC) analysis, precision-recall evaluation, and feature importance of Random Forest (RF) were used for better understanding of model performance. The findings show that ensemble tree-based methods work very effectively on these organized tabular clinica dataset.
