The Role of Artificial Intelligence in Identifying Hypertension Risk Factors

Abstract

Hypertension is one of the major health problems in the world and initial diagnosis and control is of paramount importance in avoiding cardiovascular complications. The latest developments in the area of artificial intelligence (AI) and machine learning (ML) have shown that it can be efficiently used to predict, diagnose, and treat hypertension by utilizing data-driven solutions. Neural networks, ensemble classifiers, and explainable AI (XAI) are examples of AI models that utilize various patient data, including age, lifestyle, clinical measurements, and images to identify patients at risk correctly. A number of studies have noted the usefulness of predictive models in risk stratification of hypertension, enhancing patient engagement, as well as helping clinicians make individual decisions. Furthermore, multimodal AI algorithms are based on the combination of health records with wearable sensor data and laboratory parameters to improve predictive accuracy. Issues like data quality, model explainability and population heterogeneity persist, but explainable AI usage and cross-validation methods demonstrate the potential that can be used to overcome them. This review summarizes the results of the recent research with a specific focus on the use of AI-based hypertension risk prediction, patient monitoring, and clinical decision support systems. One of the future perspectives is the combination of AI and telemedicine with real-time monitoring to allow proactive control and individual intervention in hypertensive patients.

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