ArticleComputational and mathematical methods in medicine2022
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 76 citations in OpenAlex.
- Fine tuned CatBoost machine learning approach for early detection of cardiovascular disease through predictive modeling.Scientific reports · 2025Article
- Expression of Concern: Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.PLOS digital health · 2025Article
- Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.PloS one · 2025Article
- Developing in hospital mortality prediction model tools for patients with acute myocardial infarction (STEMI) using Yazd cardiovascular disease registry, YCDR data.ARYA atherosclerosis · 2025Article
- Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk.Diabetology & metabolic syndrome · 2024Article
- Tongue color parameters in predicting the degree of coronary stenosis: a retrospective cohort study of 282 patients with coronary angiography.Frontiers in cardiovascular medicine · 2024Article
- Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.PLOS digital health · 2023Article
- Article
- Prediction of Heart Disease Based on Machine Learning Using Jellyfish Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach.Healthcare informatics research · 2023Article
- Retracted: Implementation of a Heart Disease Risk Prediction Model Using Machine Learning.Computational and mathematical methods in medicine · 2023Article
Corrections and comments
- Retraction · 2023-07-19Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.