Research on Data Mining in Healthcare focuses on leveraging advanced computational techniques to extract meaningful insights from vast and complex medical datasets. Recent developments include the use of federated learning to enable collaborative model training across multiple institutions while preserving patient privacy, and multi-aspect pretraining frameworks like MPLite that integrate structured medical records and laboratory results to improve diagnosis prediction. Additionally, synthetic data generation methods are employed to address data scarcity and privacy concerns, facilitating robust model development and validation. Overall, these innovations aim to enhance predictive analytics, support personalized medicine, and improve clinical decision-making by bridging data-driven insights with practical healthcare applications.