The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm
Open accessAbstract
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Witten, I. H. (2008) · Cited by 9,143
(2008) · Data Mining and Market Intelligence for Optimal Marketing Returns · doi:10.4324/9780080878096-12 · Cited by 6,954
An Introduction to Data Mining
Stephens, C. R., & Sukumar, R. (2006) · The Handbook of Marketing Research · doi:10.4135/9781412973380.n22 · Cited by 4,562
Introduction to Evolutionary Computing
Eiben, A. E., & Smith, J. E. (2003) · Natural Computing Series · doi:10.1007/978-3-662-05094-1 · Cited by 4,176
Braunwald's Heart Disease: A Textbook of Cardiovascular Medicine
Mann, D. L., Zipes, D. P., Libby, P., Bonow, R. O., & D, E. B. M. (2011) · Medical Entomology and Zoology · Cited by 3,745
Feature extraction : foundations and applications
Guyon, I., Gunn, S. R., Nikravesh, M., & zadeh, L. N. (2006) · Cited by 1,658
Introduction to Statistical Pattern Recognition
(2011) · Statistical Pattern Recognition · doi:10.1002/9781119952954.ch1 · Cited by 1,597
Cited by
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
Srinivasa Rao, A. S. R., & Vazquez, J. A. (2020) · Infection Control & Hospital Epidemiology · doi:10.1017/ice.2020.61 · Cited by 394
Drought forecasting by ANN, ANFIS, and SVM and comparison of the models
Mokhtarzad, M., Eskandari, F., Jamshidi Vanjani, N., & Arabasadi, A. (2017) · Environmental Earth Sciences · doi:10.1007/s12665-017-7064-0 · Cited by 203
Vijayashree, J., & Sultana, H. P. (2018) · Programming and Computer Software · doi:10.1134/s0361768818060129 · Cited by 130
Development of various diabetes prediction models using machine learning techniques
Shin, J., Kim, J., Lee, C., Yoon, J. Y., Kim, S., Song, S., & Kim, H.-S. (2022) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2021.0115 · Cited by 29
Metadata
- Authors
- Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 141
- Pages
- 19-26
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2579725890
- Cited by
- 499
- References
- 59
- FWCI
- 49.80
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. https://app.cochat.ai/papers/10.1016/j.cmpb.2017.01.004
- The paper
- Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017). Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2017.01.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
