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Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data.
Sara Montagna1, Martino Francesco Pengo2,3, Stefano Ferretti4
1DiSPeA-University of Urbino Carlo Bo, Piazza della Repubblica 13, Urbino, 61029, Italy. sara.montagna@uniurb.it.
Journal of Medical Systems
|December 29, 2022
Summary
Machine learning (ML) algorithms show promise for improving hypertension detection. While no single ML model perfectly outperformed others, Random Forest demonstrated better prediction accuracy than current medical screening protocols for identifying individuals at risk of high blood pressure.
Area of Science:
- Cardiology
- Medical Informatics
- Data Science
Background:
- Hypertension is a major risk factor for cardiovascular events and mortality.
- Current screening programs often fail to identify individuals at high risk.
- Improved strategies are needed for early and accurate hypertension detection.
Purpose of the Study:
- To investigate the potential of machine learning (ML) algorithms in identifying predictors of hypertension.
- To compare the performance of ML models against existing medical screening protocols.
Main Methods:
- Analysis of a large dataset (20,206 individuals) from World Hypertension Day questionnaires (2015-2019).
- Testing five different ML algorithms with various balancing techniques.
- Evaluation of ML model performance against the current standard medical protocol.
Main Results:
- No single ML algorithm configuration significantly outperformed all others.
- Random Forest model showed promising results with 0.818 sensitivity and 0.629 specificity.
- Current medical protocols had higher sensitivity (0.906) but significantly lower specificity (0.230).
Conclusions:
- Machine learning approaches can enhance the precision and cost-effectiveness of hypertension screening programs.
- Accurate data collection is crucial for effective ML model performance.
- Further research is needed to identify new features and improve ML model accuracy for hypertension prediction.
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