PhysioPlus Open
Forecasting Cardiovascular Health Indicators with Single and Hybrid Deep Learning Models
By Kashif Sharif, Aima Iqbal and Muhammad Rashid.
Abstract
Objective
To compare single and hybrid deep learning models for forecasting six aggregate cardiovascular health indicators at two-, five-, and ten-year horizons.
Methods
Convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and three hybrid architectures were compared for hypertension prevalence, mean systolic blood pressure, diabetes prevalence, mean total cholesterol, and urban and rural mean body mass index. Forecast errors were assessed using mean absolute error, mean absolute percentage error, and root mean squared error. The manuscript also describes a separate patient-level heart disease classification workflow using two public datasets, recursive feature elimination, classical machine-learning models, and a stacking ensemble with a support vector machine meta-learner.