A Comprehensive Review of Machine Learning Techniques for Early Diagnosis of Cardiovascular Disease

Abstract

Heart diseases (CVDs) continue to be significant data has enabled the development of advanced ML models causes of morbidity in the world. mortality, and emphasizing the necessity of early, correct and prognostic and diagnostic systems that are ethically right. Recent improvements in artificial intelligence (AI) and machine. ML have facilitated the creation of data-driven learning (ML). clinical decision support models that can improve. Early disease diagnosis, risk prioritization and personalized treatment planning. The review summarizes current articles (2022-2025) that pay attention to ML- and AI- based. CVD risk prediction methods, CVD diagnosis methods. Assessment of myocardial ischemia, CVD early diagnosis, and. purposive treatment recommendation. The reviewed works use nonhomogeneous data, such as open repositories, large-scale real world, multi-institutional benchmarks. Hospital and ICU data. These studies are methodologically the same. supervised, ensemble, deep learning. learning architectures and hybrid models of ML and. DL and reinforcement learning of sequential clinical decision-making. Some of the studies highlight explainable AI. (XAI) methods and moral aspects to improve on. Clinical trust and safety. In comparison with other businesses, it implies that. Hybrid structures and ensemble structures frequently perform better. Stronger than predictive performance and strength. Separate models on benchmark data. However, problems connected with interpretability, extrinsic validation, fairness, and real-time clinical deployment are maintained. The review concludes with naming of important gaps in research and pointing out. Prospects at glorifiable, ethical and scalable AI. Effective real world healthcare systems.

Ähnliche Arbeiten