Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations
Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/15/1/94 |
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| author | Dimitrios-Ioannis Kasartzian Thomas Tsiampalis |
| author_facet | Dimitrios-Ioannis Kasartzian Thomas Tsiampalis |
| author_sort | Dimitrios-Ioannis Kasartzian |
| collection | DOAJ |
| description | Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intelligence (AI) approaches represent a paradigm shift in risk prediction, offering dynamic, scalable solutions that integrate diverse data types. This review examines advancements in AI/ML for CVD risk prediction, analyzing their strengths, limitations, and the challenges associated with their clinical integration. Recommendations for standardization, validation, and future research directions are provided to unlock the potential of these technologies in transforming precision cardiovascular medicine. |
| format | Article |
| id | doaj-art-4a401d82e563442f90271f7c0b53cccd |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-4a401d82e563442f90271f7c0b53cccd2025-01-24T13:38:45ZengMDPI AGLife2075-17292025-01-011519410.3390/life15010094Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence InnovationsDimitrios-Ioannis Kasartzian0Thomas Tsiampalis1Department of Nutrition and Dietetics, School of Physical Education, Sports and Dietetics, University of Thessaly, 42132 Trikala, GreeceDepartment of Nutrition and Dietetics, School of Physical Education, Sports and Dietetics, University of Thessaly, 42132 Trikala, GreeceCardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intelligence (AI) approaches represent a paradigm shift in risk prediction, offering dynamic, scalable solutions that integrate diverse data types. This review examines advancements in AI/ML for CVD risk prediction, analyzing their strengths, limitations, and the challenges associated with their clinical integration. Recommendations for standardization, validation, and future research directions are provided to unlock the potential of these technologies in transforming precision cardiovascular medicine.https://www.mdpi.com/2075-1729/15/1/94cardiovascular diseaserisk predictionmachine learningartificial intelligencedeep learningprecision medicine |
| spellingShingle | Dimitrios-Ioannis Kasartzian Thomas Tsiampalis Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations Life cardiovascular disease risk prediction machine learning artificial intelligence deep learning precision medicine |
| title | Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations |
| title_full | Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations |
| title_fullStr | Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations |
| title_full_unstemmed | Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations |
| title_short | Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations |
| title_sort | transforming cardiovascular risk prediction a review of machine learning and artificial intelligence innovations |
| topic | cardiovascular disease risk prediction machine learning artificial intelligence deep learning precision medicine |
| url | https://www.mdpi.com/2075-1729/15/1/94 |
| work_keys_str_mv | AT dimitriosioanniskasartzian transformingcardiovascularriskpredictionareviewofmachinelearningandartificialintelligenceinnovations AT thomastsiampalis transformingcardiovascularriskpredictionareviewofmachinelearningandartificialintelligenceinnovations |