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

M. Haider Abu Yazid (Universiti Teknologi Malaysia (UTM))
Shukor Talib (Universiti Teknologi Malaysia (UTM))
Muhammad Haikal Satria (Universiti Teknologi Malaysia (UTM))
Azmee Abd Ghazi (National Heart Institute)

Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 4: EECSI 2017

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.