Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset

Abstract

Hypertension is a serious medical condition that significantly increases the risk of chronic diseases. Early detection of individuals at risk for hypertension allows to prevent and delay the incidence of related diseases and strokes. In recent years, numerous researches have been focused on the decision support system for predicting hypertension. However, the class imbalance has commonly occurred problem in real-world applications. In this paper, we present the end-to-end cost-sensitive neural network (COST-NN) framework incorporated with a weighted random forest-based feature selection technique to predict hypertension among Korean adults. First, it identifies the best representative features using a weighted random forest-based feature selection technique. Then, we apply the COST-NN for predicting target among hypertensive and non-hypertensive individuals. In order to identify the accurate predictive model, we compare the various baseline models. Experimental results showed that COST-NN outperforms the regular state-of-the-art baseline models. In addition, the presented framework is expected to apply not only hypertension but also can support to prevent the patients from the risk of various diseases.