Key result
Adaptive Boosting and logistic regression ensemble predicts 5-year incident hypertension with ~0.90 AUC.
Why the study?
Hypertension is a key contributor to worldwide cardiovascular mortality, motivating the use of artificial intelligence for early and accurate hypertension prediction across diverse populations.
Does an ensemble machine learning model accurately predict incident hypertension within 5 years using regular health checkup data in adults?
Cohort (n=1,541,463)
Yes
Does an ensemble machine learning model accurately predict incident hypertension within 5 years using regular health checkup data in adults?
An ensemble machine learning model using basic health checkup data (age, blood pressure, BMI, fasting glucose) can accurately predict incident hypertension within 5 years across diverse East Asian populations.
May support 5-year hypertension risk stratification from routine data; leaves open prospective validation before clinical use.
BACKGROUND Worldwide, cardiovascular diseases are the primary cause of death, with hypertension as a key contributor. In 2019, cardiovascular diseases led to 17.9 million deaths, predicted to reach 23 million by 2030. OBJECTIVE This study presents a new method to predict hypertension using demographic data, using 6 machine learning models for enhanced reliability and applicability. The goal is to harness artificial intelligence for early and accurate hypertension diagnosis across diverse populations. METHODS Data from 2 national cohort studies, National Health Insurance Service-National Sample Cohort (South Korea, n=244,814), conducted between 2002 and 2013 were used to train and test machine learning models designed to anticipate incident hypertension within 5 years of a health checkup involving those aged ≥20 years, and Japanese Medical Data Center cohort (Japan, n=1,296,649) were used for extra validation. An ensemble from 6 diverse machine learning models was used to identify the 5 most salient features contributing to hypertension by presenting a feature importance analysis to confirm the contribution of each future. RESULTS The Adaptive Boosting and logistic regression ensemble showed superior balanced accuracy (0.812, sensitivity 0.806, specificity 0.818, and area under the receiver operating characteristic curve 0.901). The 5 key hypertension indicators were age, diastolic blood pressure, BMI, systolic blood pressure, and fasting blood glucose. The Japanese Medical Data Center cohort dataset (extra validation set) corroborated these findings (balanced accuracy 0.741 and area under the receiver operating characteristic curve 0.824). The ensemble model was integrated into a public web portal for predicting hypertension onset based on health checkup data. CONCLUSIONS Comparative evaluation of our machine learning models against classical statistical models across 2 distinct studies emphasized the former’s enhanced stability, generalizability, and reproducibility in predicting hypertension onset. CLINICALTRIAL
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Hwang et al. (2023) conducted a cohort in Incident hypertension (n=1,541,463). Machine learning models (Adaptive Boosting and logistic regression ensemble) vs. Classical statistical models was evaluated on Incident hypertension within 5 years (AUC 0.901). An Adaptive Boosting and logistic regression ensemble model predicted incident hypertension within 5 years with a balanced accuracy of 0.812 and an AUC of 0.901.
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