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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
M. Haider Abu Yazid
Universiti Teknologi Malaysia (UTM)
Computer Science & IT Electrical & Electronics Engineering

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Neural Network on Mortality Prediction for the Patient Admitted with ADHF (Acute Decompensated Heart Failure) M. Haider Abu Yazid; Shukor Talib; Muhammad Haikal Satria; Azmee Abd Ghazi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.021 KB) | DOI: 10.11591/eecsi.v4.1017

Abstract

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.
Co-Authors Azmee Abd Ghazi Muhammad Haikal Satria Shukor Talib