The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Heart Disease Prediction using Machine Learning Techniques
Abstract
Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.
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
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174944 · Cited by 1,346
Growing Epidemic of Coronary Heart Disease in Low- and Middle-Income Countries
Gaziano, T. A., Bitton, A., Anand, S., Abrahams-Gessel, S., & Murphy, A. (2010) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2009.10.002 · Cited by 1,176
Survey of Machine Learning Algorithms for Disease Diagnostic
Fatima, M., & Pasha, M. (2017) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2017.91001 · Cited by 627
The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease
Seckeler, M. D., & Hoke, T. (2011) · Clinical Epidemiology · doi:10.2147/clep.s12977 · Cited by 500
Performance evaluation of different machine learning techniques for prediction of heart disease
Dwivedi, A. K. (2018) · Neural Computing and Applications · doi:10.1007/s00521-016-2604-1 · Cited by 386
Heart disease prediction using machine learning techniques : a survey
V. Ramalingam, V, Dandapath, A., & Karthik Raja, M. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i2.8.10557 · Cited by 368
Heart Disease Prediction using Machine learning and Data Mining Technique
Xavier, A., Sadat, S., & Chakalakal, S. (2021) · Cited by 203
Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients
Parthiban, G., & Srivatsa, S. K. (2012) · International Journal of Applied Information Systems · doi:10.5120/ijais12-450593 · Cited by 152
Data Mining Approach to Detect Heart DiseasesPreprint
Chaurasia, V., & Pal, S. (2014) · SSRN Electronic Journal · Cited by 116
Predictive analytics to prevent and control chronic diseases
Deepika, K., & Seema, S. (2016) · International Conference on Applied and Theoretical Computing and Communication Technology · doi:10.1109/icatcct.2016.7912028 · Cited by 103
Cited by
Effective Heart Disease Prediction Using Machine Learning Techniques
Bhatt, C. M., Patel, P., Ghetia, T., & Mazzeo, P. L. (2023) · Algorithms · doi:10.3390/a16020088 · Cited by 586
AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview
Stylianides, C., Nicolaou, A., Sulaiman, W. A., Alexandropoulou, C.-A., Panagiotopoulos, I., Karathanasopoulou, K., Dimitrakopoulos, G., Kleanthous, S., Politi, E., Ntalaperas, D., et al. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010006 · Cited by 28
Bedi, P., Thukral, A., & Dhiman, S. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02465-8 · Cited by 10
Metadata
- Authors
- Shah, D., Patel, S., & Bharti, S. K.
- Venue
- SN Computer Science
- Published
- 2020-10-16
- Volume
- 1
- Issue
- 6
- ISSN
- 2661-8907 (ISSN-L)
- 2662-995X
- OpenAlex
- W3092745004
- Cited by
- 658
- References
- 12
- FWCI
- 58.42
- Type
- journal article
- Language
- en
- Open access
- closed
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: Heart Disease Prediction using Machine Learning Techniques. https://app.cochat.ai/papers/10.1007/s42979-020-00365-y
- The paper
- Shah, D., Patel, S., & Bharti, S. K. (2020). Heart Disease Prediction using Machine Learning Techniques. SN Computer Science. https://doi.org/10.1007/s42979-020-00365-y
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.
