Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Hyerim Kim, Seunghyeon Hwang, Suwon Lee () and Yoona Kim ()
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Hyerim Kim: Department of Food and Nutrition, Gyeongsang National University, Jinju 52828, Republic of Korea
Seunghyeon Hwang: Department of Computer Science, Gyeongsang National University, Jinju 52828, Republic of Korea
Suwon Lee: Department of Computer Science, The Research Institute of Natural Science, Gyeongsang National University, Jinju 52828, Republic of Korea
Yoona Kim: Department of Food and Nutrition, Institute of Agriculture and Life Science, Gyeongsang National University, Jinju 52828, Republic of Korea

IJERPH, 2022, vol. 19, issue 22, 1-20

Abstract: 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.

Keywords: deep learning; deep neural network; hypertension; decision tree; nutrient and dietary pattern; energy intake adjustment (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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