ArticleBiology2023
Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods.
Article in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 90 citations in OpenAlex.
- Analysis of chemiluminescence and liquid chromatography-mass spectrometry in 25-hydroxyvitamin D detection using fuzzy logic.Scientific reports · 2026Article
- Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open issues.Frontiers in digital health · 2026Review
- Pre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease.The Eurasian journal of medicine · 2025Article
- Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.BMC public health · 2025Article
- Fine tuned CatBoost machine learning approach for early detection of cardiovascular disease through predictive modeling.Scientific reports · 2025Article
- Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach.Social psychiatry and psychiatric epidemiology · 2025Article
- OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Early heart disease prediction using LV-PSO and Fuzzy Inference Xception Convolution Neural Network on phonocardiogram signals.Frontiers in neuroinformatics · 2025Article
- lncRNA HOTAIR and Cardiovascular diseases.Functional & integrative genomics · 2024Review
- CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.Bioengineering (Basel, Switzerland) · 2024Article
- Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.BMC medical informatics and decision making · 2024Article
- Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology.Diagnostics (Basel, Switzerland) · 2024Article
- On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.Biomedicines · 2023Article
- Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient's age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.
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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.