ArticleHealthcare informatics research2016
Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier.
Article in Healthcare informatics research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 114 citations in OpenAlex.
- Pooled it
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- Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.Frontiers in cardiovascular medicine · 2024Article
- Thoracic Aortic Aneurysm Risk Assessment: A Machine Learning Approach.JACC. Advances · 2023Article
- Research on recognition and classification of pulse signal features based on EPNCC.Scientific reports · 2022Article
- Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury.Scientific reports · 2022Article
- Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data.Diagnostics (Basel, Switzerland) · 2022Article
- Association and Risk Factors for Obstructive Sleep Apnea and Cardiovascular Diseases: A Systematic Review.Diseases (Basel, Switzerland) · 2021Review
- Article
- Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine.Computational intelligence and neuroscience · 2021Article
- Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography.International journal of environmental research and public health · 2020Article
- Risk stratification in pulmonary arterial hypertension using Bayesian analysis.The European respiratory journal · 2020Article
- Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020Article
- Prediction of postoperative complications of pediatric cataract patients using data mining.Journal of translational medicine · 2019Article
- Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model.International journal of ophthalmology · 2019Article
- Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms.Healthcare informatics research · 2018Article
- Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Machine Learning to Compare Frequent Medical Problems of African American and Caucasian Diabetic Kidney Patients.Healthcare informatics research · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults.
methodsThe process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3.
resultsThe evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease.
conclusionsThe research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.
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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.