Traditional vs AI models to predict hypertension risk

High blood pressure is a serious health problem around the world, leading to many heart-related illnesses and deaths. The usual ways of predicting who’s at risk often rely on general stats and common risk factors, but they don’t always consider individual differences.

Jakub Nalepa, Machine Learning Architect at Graylight Imaging is a co-author of a publication focused on Artificial Intelligence and Digital Twins for the Personalized Prediction of Hypertension Risk. The review explores how AI and ML can enhance hypertension risk prediction by integrating diverse data sources, including clinical records, lifestyle factors, and genetic information. It brings together current approaches, identifies key limitations, and highlights the potential of AI-driven, personalized strategies for hypertension prevention and management, while emphasizing the importance of reproducibility and transparency for successful clinical adoption.

Let’s take a closer look at the most important aspects related to the use of artificial intelligence in this field.

Traditional statistical approaches involve the use of clinically relevant variables to explain associations. This is intended to aid in understanding underlying biological and pathophysiological mechanisms. However, these variables don’t always reflect the full picture, missing key influences like genetics, environment, and other personal factors. As a result, these approaches can miss important drivers of the disease, offering only a partial view of how hypertension develops and progresses.

Table 1. Common Statistical Techniques Used for Hypertension Risk Estimation

Compared to traditional statistical models, AI and machine learning approaches are more flexible and scalable, and they don’t depend as heavily on assumptions like normality, linear relationships, or equal variances. These models are typically validated using separate datasets during development, and they’re well-suited to identifying which variables play the most important role in predicting risk.

AI and machine learning techniques have also shown superiority over traditional statistical methods by reducing bias, automatically handling missing data, controlling for confounding factors, and managing imbalanced datasets—critical elements for building accurate models. For example, Wu et al. used extreme gradient boosting, an AI/ML technique, to predict clinical outcomes in young hypertensive patients (aged 14–39) and achieved higher concordance scores compared to traditional models like the Cox proportional hazards regression and the recalibrated Framingham risk score. Similarly, a study from Japan used an ensemble AI/ML approach to predict new cases of hypertension, outperforming a regression-based classification model.

Table 2. AI-Based Methods Applied to Hypertension Studies

The publication highlighted several weaknesses in current methods for personalized hypertension risk prediction, especially regarding the quality of data sources and the clarity of model explanations. These challenges emphasize the need for more advanced techniques capable of processing complex, unstructured data. In this context, the results show that tree-based ensemble models are the most commonly used across studies.

While traditional AI and machine learning algorithms remain the foundation of data-driven approaches, deep learning models with higher capacity are still rarely applied. These deep learning methods offer the ability to automatically learn data representations, which is especially valuable when working with unstructured clinical data where manual feature engineering often falls short. Using deep learning could provide deeper insights and enhance predictive accuracy in this area.

Deep learning’s strength lies in its ability to handle complex and varied data types, making it especially well-suited for analyzing the intricate and diverse nature of genomic data, which can drive advances in precision medicine. However, its use with genomic data is limited by the need for large, high-quality datasets that are often hard to obtain. Similarly, deep learning models can accurately identify accelerometer wear-sites and activity intensity directly from raw data, helping to overcome challenges related to where wearables are placed. Yet, these approaches depend on strong models and consistent input, and factors like inconsistent device use and participant compliance create additional hurdles. This variability makes it challenging to reliably apply these methods in physical activity studies, which in turn affects their effectiveness for predicting hypertension risk.

Key strengths and challenges of applying deep learning to genomic data and physical activity research in hypertension prediction include:

• Deep learning excels at processing complex and diverse data types, making it ideal for analyzing the intricate and varied nature of genomic data, which supports advances in precision medicine.
• Its application to genomic data is limited by the requirement for large, high-quality datasets, which are often difficult to obtain.
• Deep learning can accurately classify accelerometer wear-sites and activity intensity directly from raw acceleration data, helping to overcome challenges related to wearable placement.
• These methods rely heavily on robust models and consistent input data for reliable performance.
• Variability in device usage and participant adherence introduces challenges, complicating the effective use of deep learning approaches in physical activity research.
• Such inconsistencies impact the reliability of these methods when applied to hypertension risk prediction.

The successful integration of AI/ML into medical data analysis, particularly in hypertension management, depends on meeting five key quality criteria: reproducibility, clear intended use, rigorous validation, adequate sample size, and openness of data and software. While recent studies show progress in including larger patient samples for validation, many lack transparency, making their results difficult to reproduce or compare. To advance the field and ensure clinical relevance, researchers, authors, and reviewers must commit to these standards, promoting thorough reporting, clear model purpose, strong validation, sufficient data, and open access to code and datasets. This commitment will improve the quality and impact of AI-driven hypertension prediction and disease management.

Source: Artificial intelligence and digital twins for the personalised prediction of hypertension risk, https://doi.org/10.1016/j.compbiomed.2025.110718