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 language | English |
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Title of host publication | Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference |
Editors | Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga |
Publisher | Springer Verlag |
Pages | 1250-1257 |
Number of pages | 8 |
ISBN (Print) | 9783030237554 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019 |
Publication series
Name | Advances in Intelligent Systems and Computing |
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Volume | 1029 |
ISSN (Print) | 2194-5357 |
ISSN (Electronic) | 2194-5365 |
Conference
Conference | International Conference on Intelligent and Fuzzy Systems, INFUS 2019 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 23/07/19 → 25/07/19 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- Artificial neural network
- Classification
- Genetic algorithm
- Heart disease