System Diagnosis of Coronary Heart Disease using A Combination of Dimensional Reduction and Data Mining Techniques: A Review
Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2017-08-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02263 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs8647_7311 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Wiharto, Wiharto |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Kusnanto, Hari |e author |
| 700 | 1 | 0 | |a Herianto, Herianto |e author |
| 245 | 0 | 0 | |a System Diagnosis of Coronary Heart Disease using A Combination of Dimensional Reduction and Data Mining Techniques: A Review |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-08-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8647 | ||
| 520 | |a Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules. | ||
| 540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science (IAES) | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Medical Informatic, Machine Learning, Computational Intelligence, Telemedicine | ||
| 690 | |a Coronary heart disease, Dimension reduction, Data mining, Feature selection, Feature extraction. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 7, No 2: August 2017; 514-523 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v7.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8647/7311 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8647/7311 |z Get fulltext |