Neural Network on Mortality Prediction for the Patient Admitted with ADHF (Acute Decompensated Heart Failure)

Main Authors: Abu Yazid, M. Haider; Universiti Teknologi Malaysia (UTM), Talib, Shukor; Universiti Teknologi Malaysia (UTM), Satria, Muhammad Haikal; Universiti Teknologi Malaysia (UTM), Abd Ghazi, Azmee; National Heart Institute
Format: Article info application/pdf eJournal
Bahasa: eng
Terbitan: IAES Indonesia Section , 2017
Online Access: http://journal.portalgaruda.org/index.php/EECSI/article/view/1017
http://journal.portalgaruda.org/index.php/EECSI/article/view/1017/581
Daftar Isi:
  • Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that artificial neural network can predict mortality for ADHF patient with good prediction accuracy of 94.73% accuracy for training dataset and 91.65% for test dataset.