The DOI matches its Crossref record; 52 of the 54 references it cites were found in a registry or an index.
Association rule mining to detect factors which contribute to heart disease in males and females
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,362
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454
The American Heritage Dictionary of the English Language
Rinehart, E. C., & Morris, W. (1970) · The Journal of Higher Education · doi:10.1080/00221546.1970.11773921 · Cited by 2,471
Knowledge-Based Intelligent Information and Engineering Systems
高下, 大., タカシタ, タ., Takashita, T., Jain, L. C., イトカワ, ツ., Itokawa, T., 北須賀, 輝., キタスカ, テ., Kitasuka, T., 有次, 正., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85565-1 · Cited by 1,699
Human Physiology : From Cells to Systems
Sherwood, L. (1989) · Cited by 956
Predicting the 30-Year Risk of Cardiovascular Disease
Pencina, M. J., D’Agostino, R. B., Larson, M. G., Massaro, J. M., & Vasan, R. S. (2009) · Circulation · doi:10.1161/circulationaha.108.816694 · Cited by 885
Intelligent heart disease prediction system using data mining techniques
Palaniappan, S., & Awang, R. (2008) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2008.4493524 · Cited by 825
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M. E., Schmid, J.-J., Sandhu, S. S., Guppy, K. H., Lee, S. J., & Froelicher, V. F. (1989) · The American Journal of Cardiology · doi:10.1016/0002-9149(89)90524-9 · Cited by 758
Barrett-Connor, E. L. (1991) · JAMA · doi:10.1001/jama.265.5.627 · Cited by 679
Triglyceride Concentration and Ischemic Heart Disease
Jeppesen, J., Hein, H. O., Suadicani, P., & Gyntelberg, F. (1998) · Circulation · doi:10.1161/01.cir.97.11.1029 · Cited by 678
Effective diagnosis of heart disease through neural networks ensembles
Das, R., Turkoglu, I., & Sengur, A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.013 · Cited by 628
An expert system for detection of breast cancer based on association rules and neural network
Karabatak, M., & İnce, M. C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.02.064 · Cited by 497
The American Heritage dictionary of the English language
(2001) · Choice Reviews Online · doi:10.5860/choice.38-3049 · Cited by 340
Association Rule Discovery With the Train and Test Approach for Heart Disease Prediction
ORDÓÑEZ, C. R. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2006.864475 · Cited by 214
Finding Association Rules That Trade Support Optimally against Confidence
Scheffer, T. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44794-6_35 · Cited by 202
Caregiving Strain and Estimated Risk for Stroke and Coronary Heart Disease Among Spouse Caregivers
Haley, W. E., Roth, D. L., Howard, G., & Safford, M. M. (2010) · Stroke · doi:10.1161/strokeaha.109.568279 · Cited by 183
Computational Logic: Logic Programming and Beyond
Schreye, D. D., & Serebrenik, A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45628-7 · Cited by 181
Flint, A. J., Rexrode, K. M., Hu, F. B., Glynn, R. J., Caspard, H., Manson, J. E., Willett, W. C., & Rimm, E. B. (2010) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2010.01.001 · Cited by 179
Ferrara, A. M., Mangione, C. M., Kim, C., Marrero, D. G., Curb, D. J., Stevens, M. R., & Selby, J. V. (2007) · Diabetes Care · doi:10.2337/dc07-1244 · Cited by 166
Andersen, L. B., & Haraldsdóttir, J. (1993) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.1993.tb00748.x · Cited by 165
Prevalence of Cardiovascular Disease Risk Factors Among National Football League Players
TUCKER, A. M. (2009) · JAMA · doi:10.1001/jama.2009.716 · Cited by 159
Confirmation-Guided Discovery of First-Order Rules with Tertius
Flach, P. A., & Lachiche, N. (2001) · Machine Learning · doi:10.1023/a:1007656703224 · Cited by 131
Is the Framingham coronary heart disease absolute risk function applicable to Aboriginal people?
Wang, Z., & Hoy, W. E. (2005) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.2005.tb06577.x · Cited by 115
A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier
Avcı, E. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.02.053 · Cited by 113
Mining constrained association rules to predict heart disease
ORDÓÑEZ, C. R., Omiecinski, E. R., de Braal, L., Santana, C. A., Ezquerra, N. F., Taboada, J. A., Cooke, D. E., Krawczynska, E. G., & Garcia, E. V. (2002) · Proceedings · doi:10.1109/icdm.2001.989549 · Cited by 109
Risk factors for cardiovascular disease in women
Schenck-Gustafsson, K. G. (2009) · Maturitas · doi:10.1016/j.maturitas.2009.02.014 · Cited by 103
Computational Logic: Logic Programming and Beyond
Kakas, A. C., & Sadri, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45632-5 · Cited by 58
Huang, Z., Li, J., Su, H., Watts, G. S., & Chen, H. (2006) · Decision Support Systems · doi:10.1016/j.dss.2006.02.002 · Cited by 53
Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining
Mutter, S., Hall, M., & Frank, E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30549-1_47 · Cited by 51
Factor VII‐Phospholipid Complex in Male Survivors of Acute Myocardial Infarction
Dalaker, K., Smith, P., Arnesen, H., & Prydz, H. (1987) · Acta Medica Scandinavica · doi:10.1111/j.0954-6820.1987.tb10646.x · Cited by 47
Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining
Shin, A. M., Lee, I. H., Lee, G. H., Park, H. J., Park, H. S., Kim, Y. N., Lee, J.-J., & Kim, Y. N. (2010) · Healthcare Informatics Research · doi:10.4258/hir.2010.16.2.77 · Cited by 46
Current Diagnosis & Treatment in Cardiology
Crawford, M. H. (2002) · Cited by 41
Luukka, P., & Lampinen, J. (2010) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-12775-5_11 · Cited by 38
Heart disease classification ensemble optimization using Genetic algorithm
Fida, B., Nazir, M., Naveed, N., & Akram, S. (2011) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic.2011.6151471 · Cited by 35
CaseNet: a neural network tool for EEG waveform classification
Eberhart, R. C., Dobbins, R. W., & Webber, W. R. (2003) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbmsys.1989.47359 · Cited by 29
Use of neural networks in detecting cardiac diseases from echocardiographic images
Cios, K. J., Chen, K., & Langenderfer, R. A. (1990) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.59215 · Cited by 27
Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases
Vijaya, K., Nehemiah, H. K., Kannan, A., & Amma, N. G. B. (2010) · International Journal of Data Mining Modelling and Management · doi:10.1504/ijdmmm.2010.035565 · Cited by 25
Autonomous classifiers with understandable rule using multi-objective genetic algorithms
Kaya, M. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.10.039 · Cited by 23
Privacy-Preserving Data Mining of Medical Data Using Data Separation-Based Techniques
Kou, G., Peng, Y., Shi, Y., & Chen, Z. (2007) · Data Science Journal · doi:10.2481/dsj.6.s429 · Cited by 20
Network Intrusion Detection Using Class Association Rule Mining Based on Genetic Network Programming
Chen, C., Mabu, S., Shimada, K., & Hirasawa, K. (2010) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.20572 · Cited by 17
Ishibuchi, H., Kuwajima, I., & Nojima, Y. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74827-4_64 · Cited by 16
Family medicine : ambulatory care & prevention
Mengel, M. B., & Schwiebert, L. P. (2009) · McGraw-Hill Medical eBooks · Cited by 11
A Hybrid Classification Algorithm Evaluated on Medical Data
Michelakos, I., Papageorgiou, E. I., & Vassilakopoulos, M. G. (2010) · IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) · doi:10.1109/wetice.2010.22 · Cited by 11
Practical cardiology : evaluation and treatment of common cardiovascular disorders
Eagle, K. A., & Baliga, R. R. (2003) · Cited by 10
Algorithms for Efficiently and Effectively Using Background Knowledge in Tertius
Flach, P. A., Maraldi, V., Riguzzi, F., Gavanelli, M., & Mancini, T. (2006) · Bristol Research (University of Bristol) · Cited by 8
Associating IDS Alerts by an Improved Apriori Algorithm
Taihua, W., & Fan, G. (2010) · International Symposium on Intelligent Information Technology and Security Informatics · doi:10.1109/iitsi.2010.47 · Cited by 8
Patil, B. M., Joshi, R. C., & Toshniwal, D. (2010) · International Journal of Computational Intelligence Studies · doi:10.1504/ijcistudies.2010.034890 · Cited by 7
Research on a Distributed Network Intrusion Detection System Based on Association Rule Mining
Fu, D., Zhou, S., & Guo, P. (2009) · International Conference on Information Science and Engineering · doi:10.1109/icise.2009.929 · Cited by 4
Lei, L. (2010) · China Journal of Chinese Materia Medica · doi:10.4268/cjcmm20101635 · Cited by 4
Prophetia: Artificial Intelligence for TravelBox® Technology
Gunasekara, C., Geethal, B. B. A., Hassan, M., Mathew, C. D. T., Perera, A. S., Subasinghe, H., Welikala, Y., & Wimalasena, L. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-03156-4_3 · Cited by 1
Research works printed but not linked: 2
- Edmonds, B. H. (2005). Using localised ‘Gossip’ to structure distributed learning, centre for policy modelling. In Proceedings of the joint symposium on socially inspired computing engineering with social metaphors (AISB), University of Hertfordshire, Hatfield, UK, (pp. 127–134).
- Society, M. M. (1945). The New England journal of medicine. Massachusetts Medical Society, New England Surgical Society, vol. 232. HighWire Press new.
Cited by
Sorted by citations
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Mohan, S., Thirumalai, C., & Srivastava, G. (2019) · IEEE Access · doi:10.1109/access.2019.2923707 · Cited by 1,951
“CNN is able to generate features with various positions in the testing phase of the patient [22], [41].”
BackgroundA survey on Data Mining approaches for Healthcare
Tomar, D., & Agarwal, S. (2013) · International Journal of Bio-Science and Bio-Technology · doi:10.14257/ijbsbt.2013.5.5.25 · Cited by 502
Big data analytics for preventive medicine
Razzak, I., Imran, M. A., & Xu, G. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04095-y · Cited by 362
Machine learning-based coronary artery disease diagnosis: A comprehensive review
Alizadehsani, R., Abdar, M., Roshanzamir, M., Khosravi, A., Kebria, P. M., Khozeimeh, F., Nahavandi, S., Sarrafzadegan, N., & Acharya, U. R. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103346 · Cited by 289
Knowledge discovery in medicine: Current issue and future trend
Esfandiari, N., Babavalian, M. R., Moghadam, A. M. E., & Tabar, V. K. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.01.011 · Cited by 234
Metadata
- Authors
- Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P.
- Affiliations
- Central Queensland University (AU)
- La Trobe University (AU)
- Corresponding author
- Nahar, Jesmin
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2013-03
- Volume
- 40
- Issue
- 4
- Pages
- 1086-1093
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2080084655
- Cited by
- 394
- References
- 54
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 31 · 2023: 27 · 2022: 43 · 2021: 50 · 2020: 39 · 2019: 42 · 2018: 39 · 2017: 26 · 2016: 26 · 2015: 18 · 2014: 14 · 2013: 7
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- heart disease, coronary artery disease, association rule mining, gender differences, risk factors, Cleveland heart disease dataset, Apriori algorithm, chest pain, exercise-induced angina, resting ECG, heart disease prediction
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