Prediction of the survival of patients with cardiac failure by using soft computing techniques

Kinan Morani, Gyorgy Eigner, Tamas Ferenci, L. Kovács, Seref Naci Engin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The following paper presents a piece of work done on a relatively small dataset-with 1099 samples and 20 attributes-obtained from hospital records in Hungary. It goes to prove that by using a well tuned support vector machine model brought in better predicting results in terms of accuracy and calculation cost to a classification problem compared to an artificial neural network, random forest or the decision tree models. Next further improvements were suggested for the dataset and the preparation process as well.

Original languageEnglish
Title of host publicationSACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages201-205
Number of pages5
ISBN (Print)9781538646403
DOIs
Publication statusPublished - Aug 20 2018
Event12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018 - Timisoara, Romania
Duration: May 17 2018May 19 2018

Other

Other12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018
CountryRomania
CityTimisoara
Period5/17/185/19/18

Fingerprint

Soft computing
Soft Computing
Cardiac
Random Forest
Prediction
Decision trees
Decision tree
Classification Problems
Support vector machines
Artificial Neural Network
Support Vector Machine
Preparation
Attribute
Neural networks
Costs
Model

Keywords

  • Area Under Cover
  • Artificial Neural Networks
  • Decision Tree
  • Random Forest
  • Support Vector Machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Control and Optimization

Cite this

Morani, K., Eigner, G., Ferenci, T., Kovács, L., & Engin, S. N. (2018). Prediction of the survival of patients with cardiac failure by using soft computing techniques. In SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings (pp. 201-205). [8440931] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SACI.2018.8440931

Prediction of the survival of patients with cardiac failure by using soft computing techniques. / Morani, Kinan; Eigner, Gyorgy; Ferenci, Tamas; Kovács, L.; Engin, Seref Naci.

SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 201-205 8440931.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Morani, K, Eigner, G, Ferenci, T, Kovács, L & Engin, SN 2018, Prediction of the survival of patients with cardiac failure by using soft computing techniques. in SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings., 8440931, Institute of Electrical and Electronics Engineers Inc., pp. 201-205, 12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018, Timisoara, Romania, 5/17/18. https://doi.org/10.1109/SACI.2018.8440931
Morani K, Eigner G, Ferenci T, Kovács L, Engin SN. Prediction of the survival of patients with cardiac failure by using soft computing techniques. In SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 201-205. 8440931 https://doi.org/10.1109/SACI.2018.8440931
Morani, Kinan ; Eigner, Gyorgy ; Ferenci, Tamas ; Kovács, L. ; Engin, Seref Naci. / Prediction of the survival of patients with cardiac failure by using soft computing techniques. SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 201-205
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