Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Nov 30, 2023)
Date Submitted: Jun 6, 2023
Open Peer Review Period: Jun 6, 2023 - Jun 20, 2023
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Predicting the development of medication-treated hypertension in a large Australian working-age population using machine learning techniques
ABSTRACT
Objectives: To develop a predictive model for medication-treated hypertension using machine learning (ML) based on self-reported and claims data, and identify the top predictors for hypertension in working-age adults.
Methods:
We included 56,414 participants aged 45-55 years from the “45 and Up” Study who were free of hypertension and cardiovascular disease at baseline (2006-2009), and follow-up until December 2016. We applied four ML models: logistic regression, random forest (RF), gradient boost machine (GBM) and deep learning (DL). The aims were to access the medication-treated hypertension risk predictive models and identify the hypertension risk predictors.
Results:
Over a mean follow-up of 8.6 years, 16.0% of participants developed medication-treated hypertension. The best performing model was GBM, with an area under curve ranging from 69.2% to 79.6% at three-, five-, seven-, and ten-year follow-up. BMI was the leading hypertension predictor (contributing to 14.8% risk at year three to 39.0% risk at year ten of follow-up). Reduction in BMI from ≥30.0 to 25.0-29.9 and ≥25 to 18.5-24.9 reduced the risk of hypertension by 34.3% and 48.3%, respectively.
Conclusions:
GBM performed well in predicting the medication-treated hypertension incidence at different time points. BMI was the top contributor identified using these methods.
Citation
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