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

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. Exp

SUBMITTER: Kim H 

PROVIDER: S-EPMC9690260 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature