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Cited by 541Artificial Intelligence in HealthcareData Mining and Machine Learning ApplicationsOnline Learning and Analytics

The DOI matches its Crossref record; 21 of the 25 references it cites were found in a registry or an index.

Identification of significant features and data mining techniques in predicting heart disease

Amin, Mohammad Shafenoor, Chiam, Yin Kia, Varathan, Kasturi Dewi·2019·Telematics and Informatics
DOI 10.1016/j.tele.2018.11.007
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Abstract

Abstract Cardiovascular disease is one of the biggest cause for morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subject in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decision and prediction. There are some existing studies that applied data mining techniques in heart disease prediction. Nonetheless, studies that have given attention towards the significant features that play a vital role in predicting cardiovascular disease are limited. It is crucial to select the correct combination of significant features that can improve the performance of the prediction models. This research aims to identify significant features and data mining techniques that can improve the accuracy of predicting cardiovascular disease. Prediction models were developed using different combination of features, and seven classification techniques: k-NN, Decision Tree, Naive Bayes, Logistic Regression (LR), Support Vector Machine (SVM), Neural Network and Vote (a hybrid technique with Naive Bayes and Logistic Regression). Experiment results show that the heart disease prediction model developed using the identified significant features and the best-performing data mining technique (i.e. Vote) achieves an accuracy of 87.4% in heart disease prediction.

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Crossref · 10.1016/j.tele.2018.11.007

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Research works printed but not linked: 2

  1. Sen, 2013, A data mining technique for prediction of coronary heart disease using neuro-fuzzy integrated approach two level, Int. J. Eng. Comput. Sci. (IJECS), 2, 2663
  2. Shouman, 2013, Integrating clustering with different data mining techniques in the diagnosis of heart disease, J. Comput. Sci. Eng., 20

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Metadata

Authors
Amin, M. S., Chiam, Y. K., & Varathan, K. D.
Affiliations
University of Malaya (MY)
Corresponding author
Chiam, Yin Kia
ORCID
Venue
Telematics and Informatics
Publisher
Elsevier BV
Published
2019-03
Volume
36
Pages
82-93
ISSN
0736-5853 (ISSN-L)
DOI
10.1016/j.tele.2018.11.007
OpenAlex
W2900794383
Cited by
528
References
25
Citations by year
2026: 39 · 2025: 48 · 2024: 73 · 2023: 102 · 2022: 100 · 2021: 90 · 2020: 59 · 2019: 17
FWCI
52.69
Type
journal article
Language
en
Update notices
erratum (2022-05-01)
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Telematics and Informatics (publishedVersion)
  • The University of Malaya Research Repository (University of Malaya) (submittedVersion)
Funders
  • Ministry of Higher Education, Malaysia (FP057-2017A)
  • Universiti Malaya (RP028C-14HTM)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
heart disease prediction, cardiovascular disease, feature selection, medical data mining, classification methods, voting classifier, naive Bayes, logistic regression, support vector machine, artificial neural networks, decision tree, k-nearest neighbors

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CoChat. (2026). Citation check: Identification of significant features and data mining techniques in predicting heart disease. https://app.cochat.ai/papers/10.1016/j.tele.2018.11.007
The paper
Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics. https://doi.org/10.1016/j.tele.2018.11.007

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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