Classification of Elderly Group with Hypertension for Preventing Cardiovascular Disease Complication

Abstract

Nowadays, the amount of elderly people in the world population is increasing dramatically, so their health problem is the main concern. Cardiovascular Disease (CVD) is one of the major health problems for elderly people. Especially, hypertension is the main risk factor causing CVD. Consequently, recognizing the ability to control hypertension is very important for providing appropriate treatment recommendations. Therefore, this paper proposes a classification of an elderly group respecting the controllability of hypertension for preventing CVD complications. Decision tree, artificial neuron network, and K-nearest neighbors are employed and compared for classifying elderly group. Moreover, this paper also aims to find the most suitable classifier for the datasets used in this study. Total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, body mass index, and smoking are considered in this paper as the significant risk factors of hypertension development. All data of these factors are collected from secondary data related to elderly people having hypertension with the number of 748 datasets. Finally, the elderly people are classified into two groups including potential controllable group and potential uncontrollable group. The results show that the decision tree provides the highest accuracy of classification for this dataset with 99.11%.