skip to main content
10.1145/2975167.2985645acmconferencesArticle/Chapter ViewAbstractPublication PagesbcbConference Proceedingsconference-collections
poster

Analysis of Neurooncological Data to Predict Success of Operation Through Classification

Published: 02 October 2016 Publication History

Abstract

Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.

References

[1]
Lavrač, Nada. "Selected techniques for data mining in medicine." Artificial intelligence in medicine 16, no. 1 (1999): 3--23.
[2]
French, Nick, and Simon French. "Decision theory and real estate investment. "Journal of Property Valuation and Investment 15, no. 3 (1997): 226--232.
[3]
Lucas, Peter. "Logic engineering in medicine." Knowledge Engineering Review10 (1995): 153--180.
[4]
Cooper, Gregory F. "The computational complexity of probabilistic inference using Bayesian belief networks." Artificial intelligence 42, no. 2 (1990): 393--405.
[5]
Bratko, Ivan, and Igor Kononenko. "Learning diagnostic rules from incomplete and noisy data." Interactions in Artificial Intelligence and Statistical Methods (1987): 142--153.
[6]
Rice, Thomas W, Eugene H. Blackstone, and Valerie W. Rusch. "Of the AJCC Cancer Staging Manual: esophagus and esophagogastric junction." Annals of surgical oncology 17, no. 7 (2010): 1721--1724.
[7]
Bagherzadi, Negin. "Post Operative Prognostic Prediction Of Esophageal Cancer Cases Using Bayesian Networks And Support Vector Machines." Master diss, Middle East Technical University, 2014.
[8]
BOSWELL, DUSTIN. "Introduction to support vector machines." (2002).
[9]
Gershenson, Carlos. "Artificial neural networks for beginners." arXiv preprint cs/0308031 (2003).

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
October 2016
675 pages
ISBN:9781450342254
DOI:10.1145/2975167
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 October 2016

Check for updates

Author Tags

  1. Classifier
  2. Data Mining
  3. Multi Perceptron
  4. Naive Bayes
  5. Neuroocology
  6. Support Vector Machine

Qualifiers

  • Poster
  • Research
  • Refereed limited

Conference

BCB '16
Sponsor:

Acceptance Rates

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 63
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media