International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Heart disease is one of the most common and serious health problems in the world today. Predicting it early can help save lives by allowing people to get the right treatment on time. In this study, we compare how well machine learning and deep learning models can predict heart disease using patient data such as age, blood pressure, cholesterol level, and other health factors. Several popular machine learning algorithms like Logistic Regression, Decision Tree, Random Forest, and SVM are tested, along with deep learning models such as ANN, CNN, and RNN. Each model’s performance is measured using accuracy, precision, recall, F1-score, and ROC-AUC. Our findings show that deep learning models generally perform better in terms of accuracy and can capture complex patterns in the data more effectively. However, traditional machine learning models are easier to understand and require less computational power. Overall, this comparison helps highlight the strengths and limitations of both approaches and can guide future work in building better heart disease prediction systems.

Keywords: Heart disease prediction, Machine learning, Deep Learning, SVM, Logistic regression, Decision tree, Random Forest, ANN, CNN, RNN, Outliers, comparative analysis.

Downloads: | DOI: 10.17148/IJARCCE.2025.141236