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.
J Med Internet Res. 2024 Nov 5;26:e52794. doi: 10.2196/52794.
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.
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.
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.
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.
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.
在全球范围内，心血管疾病是主要的死亡原因，高血压是一个关键因素。2019 年，心血管疾病导致 1790 万人死亡，预计到 2030 年将达到 2300 万。
本研究提出了一种使用人口统计学数据预测高血压的新方法，使用 6 种机器学习模型以提高可靠性和适用性。目标是利用人工智能对不同人群进行早期和准确的高血压诊断。
使用来自 2 项全国队列研究的数据，即 2002 年至 2013 年进行的国家健康保险服务-国家样本队列（韩国，n=244814）和日本医疗数据中心队列（日本，n=1296649），对设计用于预测健康检查后 5 年内发生高血压的机器学习模型进行训练和测试，这些健康检查涉及年龄≥20 岁的人群。使用 6 种不同的机器学习模型的集成来识别导致高血压的 5 个最重要的特征，通过特征重要性分析来确认每个特征的贡献。
自适应提升和逻辑回归集成模型表现出较高的平衡准确性（0.812、敏感性 0.806、特异性 0.818 和受试者工作特征曲线下面积 0.901）。5 个关键的高血压指标是年龄、舒张压、BMI、收缩压和空腹血糖。日本医疗数据中心队列数据集（额外的验证集）证实了这些发现（平衡准确性为 0.741，受试者工作特征曲线下面积为 0.824）。该集成模型已集成到一个公共网络门户中，用于根据健康检查数据预测高血压的发病。
对我们的机器学习模型与 2 项不同研究中的经典统计模型的比较评估强调了前者在预测高血压发病方面的增强稳定性、通用性和可重复性。
