Application of Machine Learning for Cardiovascular Disease Risk Prediction

Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2023-01, Vol.2023 (1)
Main Authors: Dalal, Surjeet, Goel, Pallavi, Onyema, Edeh Michael, Alharbi, Adnan, Mahmoud, Amena, Algarni, Majed A., Awal, Halifa
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Bayesian analysis
Cardiomyopathy
Cardiovascular disease
Cardiovascular diseases
Comparative analysis
Congestive heart failure
Coronary artery disease
Data acquisition
Data mining
Datasets
Fatalities
Health services
Heart diseases
Heart failure
Hospitalization
Illnesses
Learning algorithms
Machine learning
Mathematical models
Medical records
Medical research
Medicine, Experimental
Mortality
Neural networks
Obesity
Patients
Prediction models
Regression analysis
Risk factors
Stroke
Support vector machines
Training
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Summary:Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.
ISSN:1687-5265
1687-5273
DOI:10.1155/2023/9418666