ArticleMedical archives (Sarajevo, Bosnia and Herzegovina)2020
Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.
Article in Medical archives (Sarajevo, Bosnia and Herzegovina), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CardiaTics: An explainable AI integrated heart disease diagnosis model with feature engineering and stacked ensemble approach.Journal of big data · 2026Article
- Article
- Cognitive Workload Detection of Air Traffic Controllers Based on mRMR and Fewer EEG Channels.Brain sciences · 2024Article
- Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors.Sensors (Basel, Switzerland) · 2024Article
- Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models.Sensors (Basel, Switzerland) · 2023Article
- An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe World Health Organization has estimated that 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the developed countries are due to cardiovascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients.
aimThe aim of this paper is to build and compare classification techniques for cardiovascular diseases.
methodsThe dataset contained 4270 patients and 14 attributes and it is available on the UCI data repository. The prediction is a binary outcome (event and no event). Variables of each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD).
resultsDifferent classifiers were tested. The SMOTE technique was used in order to solve the class imbalance. The cross-validation method was used in order to estimate how accurately our predictive models will perform. We evaluate our classifiers by using the following metrics: precision, recall, F1-score, Accuracy, AUC (Area Under Curve).
conclusionsBased on the resluts, the best scores have the Random Forest and Decision Tree classifiers.
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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.