Pembangunan Model Pendeteksi Risiko Preeklamsia pada Ibu Hamil dengan Menggunakan Metode Data Mining - Ilham, Muhammad; Adnyani, Ni Luh Saddhwi Saraswati; Suryadi, Kadarsah (Fakultas Teknik - Universitas Jenderal Achmad Yani, 2024)
Preeclampsia is a pregnancy complication indicated by an increase in blood pressure that occurs after 20 weeks of gestation and the presence of protein in the urine. If not treated quickly, preeclampsia can lead to maternal and fetal death. Therefore, a method that can help health workers to provide early detection of preeclampsia is needed. One method that can be used is data mining. This study was conducted with the aim of developing a model based on data mining methods that can be used as a tool to identify patients with preeclampsia and also to identify associated risk factors. This study was conducted using six data mining classification algorithms on 109 obstetric clinic patient data at the Jakarta Pondok Kopi Islamic Hospital (RSIJPK). The input features used as preeclampsia detection attributes were obtained based on the results of a literature study and consultations with obstetricians. Based on the results of the model evaluation, logistic regression has the best performance in detecting preeclampsia with accuracy value of 98% and precision level of 100%. In addition, this study also designed an application prototype that can be used by health workers to quickly detect the risk of preeclampsia in pregnant women.