Machine Learning-Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan.
Por:
Hwang SH, Lee H, Lee JH, Lee M, Koyanagi A, Smith L, Rhee SY, Yon DK and Lee J
Publicada:
5 nov 2024
Ahead of Print:
5 nov 2024
Resumen:
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.
Filiaciones:
Hwang SH:
Department of Biomedical Engineering, Kyung Hee University, Yongin, Republic of Korea
Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Lee H:
Department of Biomedical Engineering, Kyung Hee University, Yongin, Republic of Korea
Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Lee JH:
Health and Human Science, University of Southern California, Los Angeles, CA, United States
Lee M:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Koyanagi A:
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Barcelona, Spain
Smith L:
Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, United Kingdom
Rhee SY:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Republic of Korea
Yon DK:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Lee J:
Department of Biomedical Engineering, Kyung Hee University, Yongin, Republic of Korea
Open Access
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