Kumar Raman, Garg Sarvesh, Kaur Rupinder, Johar M G M, Singh Sehijpal, Menon Soumya V, Kumar Pulkit, Hadi Ali Mohammed, Hasson Shams Abbass, Lozanović Jasmina
Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, India.
Jadara Research Center, Jadara University, Irbid, Jordan.
Front Artif Intell. 2025 May 13;8:1583459. doi: 10.3389/frai.2025.1583459. eCollection 2025.
This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for early and precise diagnostic tools. ML models hold considerable potential by utilizing large-scale healthcare data to enhance predictive diagnostics. To systematically investigate this field, the literature is organized into five thematic categories such as "Heart Disease Detection and Diagnostics," "Machine Learning Models and Algorithms for Healthcare," "Feature Engineering and Optimization Techniques," "Emerging Technologies in Healthcare," and "Applications of AI Across Diseases and Conditions." The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). Several real-world case studies are presented to demonstrate the successful deployment of ML models in clinical and wearable settings. This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. The conclusions provide important insights for the development of artificial intelligence (AI) powered, clinically applicable heart disease prediction systems.
本综述全面且有条理地概述了机器学习（ML）在预测心脏病方面的应用，涵盖技术进步、挑战及未来前景。由于心血管疾病（CVD）是全球死亡的主要原因，因此迫切需要早期且精确的诊断工具。ML模型通过利用大规模医疗数据来增强预测诊断，具有巨大潜力。为了系统地研究该领域，文献被组织成五个主题类别，如“心脏病检测与诊断”“用于医疗保健的机器学习模型与算法”“特征工程与优化技术”“医疗保健中的新兴技术”以及“人工智能在各种疾病和病症中的应用”。该综述纳入了各种ML模型的性能基准测试，强调混合深度学习（DL）框架，例如卷积神经网络 - 长短期记忆（CNN - LSTM）在敏感性、特异性和曲线下面积（AUC）方面始终优于传统模型。还展示了几个实际案例研究，以证明ML模型在临床和可穿戴设备环境中的成功部署。本综述展示了ML方法从传统分类器到混合DL结构以及联邦学习（FL）框架的发展历程。它还讨论了伦理问题、数据集限制和模型透明度。结论为开发由人工智能（AI）驱动的、临床适用的心脏病预测系统提供了重要见解。
