Temporal Data Simulation and Drift-Resilient Machine Learning in Cardiovascular Disease Management: A Technical Analysis

Abstract

Cardiovascular diseases remain a leading cause of death globally, necessitating advanced tools for effective prediction, prevention, and management. Machine learning has emerged as a transformative approach in healthcare, offering solutions for risk assessment, disease progression modeling, and personalized treatment recommendations. However, the performance of ML models often deteriorates over time due to data drift—shifts in data distributions, relationships between variables, or diagnostic thresholds—posing significant challenges in dynamic healthcare environments. This article explores methods for simulating temporal data and designing machine learning infrastructures resilient to data drift, focusing on their applications in CVD management. The article examines techniques including Autoregressive Integrated Moving Average, Hidden Markov Models, and adaptive learning strategies for modeling evolving trends in cardiovascular metrics. To address data drift, the paper highlights strategies for detecting and mitigating its effects on model performance through comprehensive monitoring frameworks and validation protocols. Additionally, frameworks for integrating simulated temporal data into ML pipelines, including automated retraining workflows and continual learning systems that maintain model robustness, are reviewed. These approaches are applied in CVD to predict cardiac events, optimize treatment plans, and manage hospital resources. Ethical considerations, such as fairness in simulated datasets, privacy protection, and practical implementation challenges, are also discussed.