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Updated: Jun 25, 2025
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Published on: January 16, 2019
Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the
Gideon MacCarthy1, Raha Pazoki1,2
1Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK.
Insights
This study found that including genetic liabilities for cholesterol and LDL improved hypertension prediction models. Machine learning models incorporating genetic data offer valuable insights for hypertension classification.
Area of Science:
- Cardiovascular disease research
- Genetics and personalized medicine
- Machine learning in healthcare
Background:
- Hypertension is a major risk factor for cardiovascular diseases (CVD), contributing significantly to global mortality.
- Existing hypertension prediction models often lack genetic liability assessment.
- This study explores the impact of genetic risk factors on hypertension prediction.
Purpose of the Study:
- To develop an effective hypertension classification model using machine learning.
- To investigate the influence of genetic liability for CVD risk factors on hypertension risk.
- To compare random forest and neural network models for hypertension prediction.
Main Methods:
- Utilized genetic variants from GWAS to construct genetic liabilities for CVD risk factors.
- Applied random forest and neural network models to a dataset of 244,718 European participants.
- Evaluated models using AUC, calibration, and net reclassification improvement.
Main Results:
- Models without genetic liabilities achieved AUCs of 0.70 (random forest) and 0.72 (neural network).
- Adding genetic liabilities improved AUC for random forest but not neural network.
- The best model (random forest) achieved an AUC of 0.71, incorporating genetic liabilities for total cholesterol and LDL.
Conclusions:
- Incorporating genetic liabilities for lipids can enhance machine learning models for hypertension classification.
- Genetic data provides incremental value beyond baseline characteristics in predicting hypertension.
- This approach holds promise for personalized risk assessment in cardiovascular health.
Abstract:
Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.
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