Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot and few-shot reasoning capabilities, even though machine learning (ML) algorithms, especially ensemble approaches like Random Forest, XGBoost, LightGBM, and CatBoost, are excellent at modeling complex, non-linear patient data and routinely beat logistic regression. This research predicts cardiovascular disease using a merged dataset of 1,190 patient records, comparing traditional machine learning models (95.78% accuracy, ROC-AUC 0.96) with open-source large language models via OpenRouter APIs. Finally, a hybrid fusion of the ML ensemble and LLM reasoning under Gemini 2.5 Flash achieved the best results (96.62% accuracy, 0.97 AUC), showing that LLMs (78.9 % accuracy) work best when combined with ML models rather than used alone. Results show that ML ensembles achieved the highest performance (95.78% accuracy, ROC-AUC 0.96), while LLMs performed moderately in zero-shot (78.9%) and slightly better in few-shot (72.6%) settings. The proposed hybrid method enhanced the strength in uncertain situations, illustrating that ensemble ML is considered the best structured tabular prediction case, but it can be integrated with hybrid ML-LLM systems to provide a minor increase and open the way to more reliable clinical decision-support tools.
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
Zhejiang University School of Medicine（浙江大学医学院）；The First Affiliated Hospital（第一附属医院）；Liangzhu Laboratory（良渚实验室）；Shandong Jianzhu University（山东建筑大学）；FUXI AI Lab（伏羲人工智能实验室）；Beijing Normal University（北京师范大学）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Wuhan)（中国地质大学（武汉））；Hangzhou Baorun Biotechnology Co., Ltd.（杭州宝润生物科技有限公司）
An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments
LASTIMI, High School of Technology Mohammed V University in Rabat Sale, Morocco；LASTIMI, Mohammedia School of Engineers Mohammed V University in Rabat Rabat, Morocco；CISIEV. Faculty of Science；Technology in Marrakech Cadi Ayyad University Marrakech, Morocco
Personalized Heart Disease Detection via ECG Digital Twin Generation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Guangdong Provincial People’s Hospital, Southern Medical University（南方医科大学广东省人民医院）；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院广东省心血管病研究所、广东省医学科学院）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
Explainable AI meets Healthcare: A Study on Heart Disease Dataset
Pandit Deendayal Petroleum University（潘迪特·迪恩达亚尔石油大学）；Confirm SFI Centre for Smart Manufacturing（Confirm SFI智能制造中心）；Data Science Institute（数据科学研究所）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）
A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Houston（休斯顿大学）；Shanghai Jiao Tong University（上海交通大学）；New York University（纽约大学）；Tulane University（杜兰大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）
Heart Disease Detection using Quantum Computing and Partitioned Random Forest Methods
Damghan University（达姆甘大学）；Kuwait College of Science and Technology（科威特科学技术学院）；Vels Institute of Sciences, Technology, and Advanced Studies（韦尔斯科学、技术与高等研究院）；Universiti Malaysia Perlis（马来西亚玻璃市大学）
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction
School of Computer Science and Technology（计算机科学与技术学院）；Algoma University（阿尔戈马大学）；School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）；University of Ottawa（渥太华大学）
Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis
Engineering Bangladesh University, Dhaka-1207, Bangladesh.；Technology University, Santosh, Tangail-1902.；Engineering University of Information Technology \ Sciences, Dhaka, Bangladesh. moradul\；Dept. of Computer Science Maharishi International University, Fairfield, IA 52557, United States.；\ Bangladesh University, Dhaka-1207, Bangladesh.；a Deparment of Computer Science \& Engineering, Bangladesh University, Dhaka-1207, Bangladesh.；Tecnology University, Tangail-1902, Bangladesh.；c Deparment of Computer Science \& Engineering, University of Information Technology of Sciences, Dhaka, Bangladesh.；d Department of Computer Science,Fairfield, IA 52557, Maharishi International University, United States.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；City, University of London（伦敦城市大学）；Technical University of Munich（慕尼黑工业大学）
Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Fraunhofer Institute for Software and Systems Engineering ISST（弗劳恩霍夫软件与系统工程研究所）；Department of Computer Science, TU Dortmund University（图林根大学计算机科学系）；Department of Clinical Sciences, Swedish University of Agricultural Sciences（瑞典农业科学大学临床科学系）；Clinic for Small Animal Medicine, Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学小动物医学诊所）；Boehringer Ingelheim Vetmedica GmbH（波士顿-英氏兽医医药有限公司）；Department of Health Informatics, School of Medicine, Witten/Herdecke University（乌尔姆-赫尔德克大学医学院健康信息学系）
