Deep belief networks and Bayesian networks for prognosis of acute lymphoblastic leukemia

Fakhirah D. Ghaisani, Ratna Mufidah, Ito Wasito, Moh Faturrahman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
PublisherAssociation for Computing Machinery
Pages102-106
Number of pages5
ISBN (Electronic)9781450352840
DOIs
Publication statusPublished - 10 Aug 2017
Event2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, Korea, Republic of
Duration: 10 Aug 201713 Aug 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F132084

Conference

Conference2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
CountryKorea, Republic of
CityJeju Island
Period10/08/1713/08/17

Keywords

  • Acute lymphoblastic leukemia
  • Bayesian network
  • Cancer
  • Data integration
  • Deep belief network
  • Dimensionality reduction
  • Leukemia
  • Manifold learning
  • Microarray.

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