Why the study?
Few studies have classified and predicted hypertension using blood pressure-related determinants in a deep learning algorithm, and whether energy intake adjustment is adequate for deep learning algorithms remained unclear.
Does a deep neural network without energy intake adjustment improve hypertension classification accuracy compared to a decision tree in Korean adults?
Cross-Sectional (n=8,149)
Yes
Does a deep neural network without energy intake adjustment improve hypertension classification accuracy compared to a decision tree in Korean adults?
Absolute Event Rate: 75% vs 69%
A deep learning algorithm using baseline clinical and dietary variables can effectively classify hypertension risk, and energy intake adjustment is not required for optimal model performance.
Supports DNN hypertension classification without energy adjustment in Korean cohorts; hypothesis-generating, requiring prospective validation before clinical use.
Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this study is to develop a deep learning algorithm for the classification and prediction of hypertension with BP-related factors based on the Korean Genome and Epidemiology Study-Ansan and Ansung baseline survey. We also investigated whether energy intake adjustment is adequate for deep learning algorithms. We constructed a deep neural network (DNN) in which the number of hidden layers and the number of nodes in each hidden layer are experimentally selected, and we trained the DNN to diagnose hypertension using the dataset while varying the energy intake adjustment method in four ways. For comparison, we trained a decision tree in the same way. Experimental results showed that the DNN performs better than the decision tree in all aspects, such as having higher sensitivity, specificity, F1-score, and accuracy. In addition, we found that unlike general machine learning algorithms, including the decision tree, the DNNs perform best when energy intake is not adjusted. The result indicates that energy intake adjustment is not required when using a deep learning algorithm to classify and predict hypertension with BP-related factors.
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Kim et al. (2022) conducted a cross-sectional in Hypertension (n=8,149). Deep Neural Network (DNN) without energy intake adjustment vs. Decision tree algorithm and DNN with energy intake adjustment was evaluated on Accuracy of hypertension classification.
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