Open access Jurnal SINTA 1
Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases
International Journal of Electrical and Computer Engineering (IJECE) · Vol. 5 No. 6 (2015)
- ISSN
- 2088-8708, 2088-8708
- Published
- 2015-12-01
- Publisher
- Institute of Advanced Engineering and Science
Abstract
Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.
Collaboration network of the authors
40 authors · 96 co-authorship links · 55 across different organisations · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- Universiti Teknikal Malaysia Melaka
- University of Technology Malaysia
- Islamic Azad University, Tehran
- Damghan University
- Universitas Ahmad Dahlan
- Sultan Agung Islamic University
- Tehran University of Medical Sciences
- Other organisations
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Nik Rumzi Nik Idris | University of Technology Malaysia | 9 |
| Mohammad Jannati | University of Technology Malaysia | 9 |
| Mohd Hatta Jopri | Universiti Teknikal Malaysia Melaka | 8 |
| Mohd Junaidi Abdul Aziz | University of Technology Malaysia | 7 |
| Mustafa Manap | Universiti Teknikal Malaysia Melaka | 7 |
| Awang Jusoh | University of Technology Malaysia | 7 |
| Abdul Rahim Abdullah | Universiti Teknikal Malaysia Melaka | 6 |
| Lina Handayani | Universitas Ahmad Dahlan | 5 |
| M. Rahimi Yusoff | Universiti Teknikal Malaysia Melaka | 4 |
| Muhammad Ado | University of Technology Malaysia | 4 |
| Munawar Agus Riyadi | Diponegoro University | 4 |
| Deris Stiawan | Sriwijaya University | 4 |
| Shahreen binti Kasim | Tun Hussein Onn University of Malaysia | 3 |
| Mahmoud Zadehbagheri | Islamic Azad University, Tehran | 3 |
| Mohammad Javad Kiani | Islamic Azad University, Tehran | 3 |
| Vahdat Nazerian | University of Mazandaran | 3 |
| Auzani Jidin | Universiti Teknikal Malaysia Melaka | 2 |
| M. Ghanbari | Affiliation unknown | 2 |
| M. Najafi Khoshrodi | Affiliation unknown | 2 |
| A.S. Hussin | Universiti Teknikal Malaysia Melaka | 2 |
| Mohd Ruddin Ab Ghani | Universiti Teknikal Malaysia Melaka | 2 |
| Mehdi Dadkhah | Islamic Azad University, Tehran | 2 |
| Shahaboddin Shamshirband | University of Malaya | 2 |
| Ahmad Saudi Samosir | Lampung University | 2 |
| Ali Ajami | Affiliation unknown | 2 |
| Chibuike Peter Ohanu | University of Nigeria | 2 |
| Aiman Zakwan Jidin | Universiti Teknikal Malaysia Melaka | 1 |
| Rozana Alik | University of Technology Malaysia | 1 |
| M.F. Habban | Universiti Teknikal Malaysia Melaka | 1 |
| Muhammad Firdaus Abd Razak | University of Technology Malaysia | 1 |
| Mohamad Hamka Bin Saiful | University of Technology Malaysia | 1 |
| Abdul Rashid Husain | University of Technology Malaysia | 1 |
| Sahazati Md Rozali | Universiti Teknikal Malaysia Melaka | 1 |
| Muhammad Nizam Kamarudin | Universiti Teknikal Malaysia Melaka | 1 |
| Abdulhamid Usman Mutawakkil | University of Technology Malaysia | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.