Deteksi Dini Hipertensi pada Usia Dewasa Menggunakan Deep Neural Network Berdasarkan Data Skrining Kesehatan

Abstract

Hypertension is one of the leading causes of cardiovascular disease and contributes significantly to mortality worldwide. Early detection is essential to reduce the risk of complications and improve preventive healthcare. This study aims to develop an early hypertension detection model for adults using the Deep Neural Network (DNN) algorithm based on community health screening data collected from Posyandu Desa Tebas, Gondangwetan District, Pasuruan Regency. The dataset consisted of 214 health records with seven input variables: gender, age, body weight, height, waist circumference, systolic blood pressure, and diastolic blood pressure. Data preprocessing included data cleaning, feature selection, label encoding, standardization using StandardScaler, and dataset splitting into 80% training data and 20% testing data. The proposed DNN architecture consisted of three hidden layers with ReLU activation, a Sigmoid output layer, Adam optimizer, Binary Crossentropy loss function, Dropout, and Early Stopping to reduce overfitting. Experimental results showed that the proposed model achieved an accuracy of 88.37%, with precision of 94%, recall of 91%, and F1-score of 93% for the hypertension class. These results indicate that the Deep Neural Network model can effectively classify hypertension status and has the potential to support early screening for hypertension.