ArticlePatient preference and adherence2016
Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach.
Article in Patient preference and adherence, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 80 citations in OpenAlex.
- A narrative review on ethical considerations and challenges in AI-driven cardiology.Annals of medicine and surgery (2012) · 2025Review
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.BMC medical informatics and decision making · 2024Article
- Association of demographics, cardiovascular indicators and disability characteristics with 7-year coronary heart disease incident in persons with disabilities.BMC public health · 2023Article
- The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction.Healthcare (Basel, Switzerland) · 2022Article
- A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring.Medical & biological engineering & computing · 2021Review
- Feature selection and risk prediction for patients with coronary artery disease using data mining.Medical & biological engineering & computing · 2020Article
- Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment.Rheumatology and therapy · 2020Article
- AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus.Scientific reports · 2020Article
- Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models.Cardiovascular diagnosis and therapy · 2020Article
- Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes.Korean circulation journal · 2020Article
- Development and validation of prediction models for hypertension risks in rural Chinese populations.Journal of global health · 2019Article
- A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography.Current atherosclerosis reports · 2019Review
- Development and verification of prediction models for preventing cardiovascular diseases.PloS one · 2019Article
- Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis.Journal of healthcare engineering · 2017Article
- Cardiovascular Risk Profiling Using the Globorisk Calculator among Noncommunicable Disease Patients Attending Primary Health Centers of a Tertiary Care Teaching Hospital in South India: A Cross-Sectional Analytical Study.Indian journal of community medicine : official publication of Indian Association of Preventive & Social MedicineArticle
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeCurrently cardiovascular diseases (CVDs) are the main cause of death worldwide. Disease risk estimates can be used as prognostic information and support for treating CVDs. The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough. In this paper, a novel CVD prediction system based on machine learning is proposed.
methodsThis study has been conducted with the data of 689 patients showing symptoms of CVD. Furthermore, the dataset of 5,209 CVD patients of the famous Framingham study has been used for validation purposes. Each patient's parameters have been analyzed by physicians in order to make a diagnosis. The proposed system uses the quantum neural network for machine learning. This system learns and recognizes the pattern of CVD. The proposed system has been experimentally evaluated and compared with FRS.
resultsDuring testing, patients' data in combination with the doctors' diagnosis (predictions) are used for evaluation and validation. The proposed system achieved 98.57% accuracy in predicting the CVD risk. The CVD risk predictions by the proposed system, using the dataset of the Framingham study, confirmed the potential risk of death, deaths which actually occurred and had been recorded as due to myocardial infarction and coronary heart disease in the dataset of the Framingham study. The accuracy of the proposed system is significantly higher than FRS and other existing approaches.
conclusionThe proposed system will serve as an excellent tool for a medical practitioner in predicting the risk of CVD. This system will be serving as an aid to medical practitioners for planning better medication and treatment strategies. An early diagnosis may be effectively made by using this system. An overall accuracy of 98.57% has been achieved in predicting the risk level. The accuracy is considerably higher compared to the other existing approaches. Thus, this system must be used instead of the well-known FRS.
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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.