Integrating Advanced Sensing Technologies and Artificial Intelligence for Predicting Cardiovascular Risks: A Data-Driven Approach to Modern Healthcare

Cardiovascular disease (CVD) represents a major global health challenge. The integration of advanced sensing technologies with artificial intelligence (AI) and machine learning (ML) is increasingly recognized as a crucial advancement in addressing the global health challenge posed by CVD. This convergence enhances the prediction and treatment of major adverse cardiovascular events (MACEs), including acute coronary syndrome/ischemic heart disease (ACS/IHD), chronic heart failure (CHF), cerebrovascular accidents (CVA), and arrhythmias. The chapter examines various advanced biosensor technologies for the continuous monitoring of cardiovascular health-related physiological parameters, such as electrophysiology-based sensors, optical sensors, acoustic sensors, pressure sensors, bioimpedance sensors, chemical sensors, imaging sensors, and micro-electro-mechanical systems (MEMSs). Furthermore, the chapter explores the ML pipeline, including data preprocessing techniques like signal filtering, wavelet and Fourier transforms, principal component analysis (PCA), and convolutional neural networks (CNNs) for feature extraction from biosensor data. It also reviews state-of-the-art ML models for short-term and long-term forecasting of CVD, such as multi-horizon forecasting, the autoregressive integrated moving average (ARIMA), exponential smoothing, gradient boosting machines (GBMs), support vector regression (SVR), neural basis expansion analysis for interpretable time series forecasting (N-BEATS), Bayesian hierarchical models, logistic regression, long short-term memory (LSTM) neural networks, proportional hazards models, and hidden Markov models (HMMs). The chapter highlights challenges in data preprocessing, the need for robust algorithms to handle high-dimensional data, and the importance of data privacy and security when integrating sensor data with AI algorithms in this sensitive domain. By providing a comprehensive overview of the integration between advanced sensing technologies and AI in CVD management, the chapter showcases the latest innovations in monitoring, data processing, and AI-driven analysis for improved cardiovascular diagnosis and treatment in modern healthcare.