Research and Analysis of Heart Disease Risk Prediction Model Based on Machine Learning

This review looks at recent progress in using artificial intelligence and machine learning for heart disease prediction, focusing on three main model types: Gradient Boosted Trees, Transformer-Based Sequence Models and Artificial Neural Networks. The analysis shows that each method has its own advantages. Gradient Boosted Trees work well with structured data tables, achieving good accuracy numbers and giving clear feature importance information. Transformer models, which use attention mechanisms, perform exceptionally on large electronic health record data, capturing long-term patterns for multi-disease prediction. Artificial Neural Networks model complex relationships effectively, sometimes reaching near-perfect results on certain datasets. The review covers the datasets, data preparation steps, and evaluation metrics including accuracy, precision, recall, F1-score and AUC-ROC used in the studies. While the results seem promising, there remain significant challenges before clinical use. That is to say, people need better model understanding, reduced algorithmic bias to be fair across different groups, smooth integration into medical workflows, and strong data privacy protections. To put it simply, future success depends not just on accuracy but on developing practical, clear, fair, and secure AI decision tools to truly fight heart disease.