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Muhammad Haikal Satria
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.
A New Method for Minimizing the Unnecessary Handover in High-Speed Scenario Hoe Tung Yew; Muhammad Haikal Satria; Rindu Nurma Illahi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.489 KB) | DOI: 10.11591/eecsi.v5.1621

Abstract

The application of Wireless Local Area Network (WLAN) is limited to indoor or pedestrian walking speed environment because the small WLAN coverage will lead to the growth of unnecessary handover rate in high-speed scenario. The previously proposed traveling distance prediction based handover methods assumed mobile terminal (MT) travels at a constant speed is impractical as most of the MTs may not be traveling at constant speed in real environment. These methods have poor performance in case of acceleration because MT will leave the network earlier than the estimated time. In this paper, a new traveling distance prediction based handover scheme that is aware of MT's speed changes is proposed to overcome the limitation of the existing methods. The proposed scheme is adapted to the MT velocity and acceleration or deceleration rate. The numerical result shows that the performance of the proposed scheme is better than the existing handover methods in high-speed scenario. It keeps the probability of unnecessary handover within the user acceptable level in high-speed scenario.
Co-Authors Azmee Abd Ghazi Hoe Tung Yew M. Haider Abu Yazid Rindu Nurma Illahi Shukor Talib