Classification of hypertension disease using Artificial Neural Network (ANN) backpropagation method case study in mitigating health risk: UPT Modopuro Mojokerto Health Center

¹ Departemen of Informatics, Faculty of Engineering, University of Trunojoyo Madura, Kamal, Bangkalan, Indonesia
² Faculty of Engineering and Quantity Surveying, INTI International University, Negeri Sembilan 71800, Malaysia

^* Corresponding author: em_sari@trunojoyo.ac.id

Abstract

Hypertension is a disease caused by increased blood pressure above 140/90 mmHg and is often referred to as "the silent killer" because most sufferers do not realize that they have hypertension, and only realize when complications have occurred. Hypertension is one of the main causes of death worldwide which can be influenced by many factors. In UPT (Integrated Service Unit) PUSKESMAS (Community Health Center) Modopuro, Mojokerto Regency, hypertension is ranked among the top 10 diseases with the most patients. With a fairly high risk of death and an increase in the number of people with hypertension, it is often caused by delays in diagnosis, which must be carried out blood pressure checks by medical personnel at least 2 times with 1 week to establish a diagnosis of hypertension. If hypertension is not treated immediately, it can cause other health conditions such as kidney disease, heart disease, and stroke. Therefore, a system is needed that can be used for the classification of early detection of whether a person has hypertension or not. To overcome these problems, a system was created to classify hypertension using the Backpropagation method. Backpropagation is very effective in helping artificial neural networks learn from mistakes, allowing the system to make more accurate predictions over time. Dataset used in this study is the medical record data of UPT Puskesmas Modopuro patients with 1000 data. The results obtained the best model with a network structure of 7-5-1, learning rate 0.001, and Adam optimizer. With an accuracy of 93.50% and a loss value of 0.0697. While the precision, recall, and f1-score values are 94.00%, 93.00%, and 93.00%, respectively. With good accuracy performance, indicating that the backpropagation model can be applied in hypertension classification.