Artificial Intelligence and Ensemble Learning for Coronary Artery Disease Prediction

Abstract

coronary artery disease (CAD) continues to be a major cause of death linked to cardiovascular issues, and thus early diagnosis is crucial to enhance patient outcome and prevent unnecessary medical interventions. Machine learning (ML) and data mining are increasingly being recognized as robust predictive methods for CAD, with opportunities for early detection and preventive medicine. This article discusses the role of various ML algorithms to predict CAD and enhance diagnostic performance, emphasizing the importance of such methodologies in medicine. The methodology includes a rigorous study of ML techniques such as neural networks, decision trees, support vector machines, and ensemble techniques like Random Forest and XGBoost. The paper explains the advantages and disadvantages of these techniques based on their applications with publicly available medical datasets to predict CAD. Data balancing algorithms such as SMOTE and ADASYN are also incorporated for improving model performance. The findings reveal that ensemble techniques, particularly XGBoost, register the highest accuracy (94.7%), closely trailed by Random Forest (92.04%). Additionally, data balancing techniques also enhance model recall and specificity to make predictions even more accurate. The findings point towards the power of sophisticated machine learning algorithms for CAD detection as well as the need for preprocessing data to reach maximum model efficiency. This research demonstrates the broader significance of machine learning for transforming CAD prediction, and the potential to improve patient care, reduce healthcare costs, and facilitate a shift toward preventative therapy in cardiovascular disease management also we will discuss artificial intelligence (AI) applications and Recent advances in AI with CAD.

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