Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach

Miray Akgül, Özlen Erkal Sönmez*, Tuncay Özcan

*Corresponding author for this work

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

15 Citations (Scopus)

Abstract

Heart disease is the most important public health problem for many countries. Early diagnosis of heart disease is extremely crucial for the survival of the patient. At this point, classification algorithms are widely used for medical diagnosis. In this study, firstly, artificial neural network (ANN) with default parameters is used to diagnose heart disease. Then, a hybrid approach, combining artificial neural network (ANN) and genetic algorithm (GA), is proposed to improve classification accuracy. Finally, the effectiveness of the proposed approach is illustrated with ‘Cleveland’ dataset taken from UCI machine learning repository. Experimental results show that the proposed hybrid ANN - GA approach outperforms Naive Bayes, K- Nearest Neighbor and C4.5 algorithms in terms of accuracy rate, precision, recall and F-measure.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
PublisherSpringer Verlag
Pages1250-1257
Number of pages8
ISBN (Print)9783030237554
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey
Duration: 23 Jul 201925 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1029
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/TerritoryTurkey
CityIstanbul
Period23/07/1925/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Artificial neural network
  • Classification
  • Genetic algorithm
  • Heart disease

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