research-article

Deep Belief Networks and Bayesian Networks for Prognosis of Acute Lymphoblastic Leukemia

Authors:

Fakhirah D. Ghaisani,

Moh. Faturrahman,

Ratna MufidahAuthors Info & Claims

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

Pages 102 - 106

https://doi.org/10.1145/3127942.3127947

Published: 10 August 2017 Publication History

Abstract

Cancer is one of main non-communicable diseases. Acute Lymphoblastic Leukemia (ALL), a type of white blood cancer, is one of the most common pediatric cancers. Analysis of cancer prognosis is necessary to determine the proper treatment for each patient. However, cancer data analysis is challenging because multiple risk factors may influence the prognosis of cancer, including gene and clinical condition of patient. This study aims to develop prediction model for cancer prognosis using clinical and gene expression (microarray) data. In this research, manifold learning is applied to microarray data to reduce its dimension, then two Deep Belief Network (DBN) models for both clinical and microarray data are trained separately. Probabilities obtained from Clinical DBN model and Microarray DBN model are integrated using softmax nodes on Bayesian Network structure. Based on various experiments, the best integration model obtained is DBN+BN 32 with prediction accuracy 84.2% for 2-years survival, 70.2% for 3-years, 68.4% for 4-years, and 73.7% for 5-years. This prediction model can be used in cancer analysis and help doctor to decide proper treatment for patient.

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Cited By

Figueiredo ERosa FZanetti RDametto MBonacin R(2023)Semântica em prontuários pletrônicos para oncologia pediátrica: uma revisão integrativaJournal of Health Informatics10.59681/2175-4411.v15.i2.2023.99315:2(61-69)Online publication date: 18-Oct-2023
https://doi.org/10.59681/2175-4411.v15.i2.2023.993
WANG YKANG RCHEN Y(2021)Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computingChinese Journal of Aeronautics10.1016/j.cja.2020.07.02634:1(252-265)Online publication date: Jan-2021
https://doi.org/10.1016/j.cja.2020.07.026

Index Terms

Deep Belief Networks and Bayesian Networks for Prognosis of Acute Lymphoblastic Leukemia
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

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cover image ACM Other conferences

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

August 2017

117 pages

ISBN:9781450352840

DOI:10.1145/3127942

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2017

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Universitas Indonesia

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ICACS '17

ICACS '17: 2017 International Conference on Algorithms, Computing and Systems

August 10 - 13, 2017

Jeju Island, Republic of Korea

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Cited By

Figueiredo ERosa FZanetti RDametto MBonacin R(2023)Semântica em prontuários pletrônicos para oncologia pediátrica: uma revisão integrativaJournal of Health Informatics10.59681/2175-4411.v15.i2.2023.99315:2(61-69)Online publication date: 18-Oct-2023
https://doi.org/10.59681/2175-4411.v15.i2.2023.993
WANG YKANG RCHEN Y(2021)Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computingChinese Journal of Aeronautics10.1016/j.cja.2020.07.02634:1(252-265)Online publication date: Jan-2021
https://doi.org/10.1016/j.cja.2020.07.026

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