A review of machine learning applications in heart health
Abstract The application of machine learning in healthcare continues to gain attention as researchers attempt to prove its potential for the enhancement of diagnosis and prognosis accuracy. Although many applications of machine learning have been well studied, there remain substantial opportunities...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-025-01430-4 |
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| author | Ava Perrone Taghi M. Khoshgoftaar |
| author_facet | Ava Perrone Taghi M. Khoshgoftaar |
| author_sort | Ava Perrone |
| collection | DOAJ |
| description | Abstract The application of machine learning in healthcare continues to gain attention as researchers attempt to prove its potential for the enhancement of diagnosis and prognosis accuracy. Although many applications of machine learning have been well studied, there remain substantial opportunities for advancement. The field of healthcare holds particularly strong potential for improvement from integration with machine learning. In the future, clinicians will likely utilize machine learning to enhance the efficiency of diagnosis and prognosis, optimizing the delivery of care. This study conducts a comprehensive examination of feature selection methodologies, model architectures, and fine-tuning techniques related to diverse diagnostic and prognostic scenarios within the domain of heart health. It addresses some key gaps in earlier research, including the lack of agreement on which data sources are most effective for classifying stroke and heart attack. This review contributes an analysis of current machine learning methods in stroke and heart attack research, highlighting key gaps such as limited use of multimodal data, external validation, and class imbalance mitigation. It suggests improvements, including the adoption of advanced sampling techniques and the use of comprehensive performance metrics. The findings suggest that despite extensive research on machine learning in cardiovascular health, there are gaps to be addressed in methodologies for data collection, preprocessing, model development, evaluation, and feature engineering. |
| format | Article |
| id | doaj-art-7cbb7b16f71a49b38713405430eae3fa |
| institution | DOAJ |
| issn | 1475-925X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj-art-7cbb7b16f71a49b38713405430eae3fa2025-08-20T03:05:11ZengBMCBioMedical Engineering OnLine1475-925X2025-08-0124112910.1186/s12938-025-01430-4A review of machine learning applications in heart healthAva Perrone0Taghi M. Khoshgoftaar1College of Engineering and Computer Science, Florida Atlantic UniversityCollege of Engineering and Computer Science, Florida Atlantic UniversityAbstract The application of machine learning in healthcare continues to gain attention as researchers attempt to prove its potential for the enhancement of diagnosis and prognosis accuracy. Although many applications of machine learning have been well studied, there remain substantial opportunities for advancement. The field of healthcare holds particularly strong potential for improvement from integration with machine learning. In the future, clinicians will likely utilize machine learning to enhance the efficiency of diagnosis and prognosis, optimizing the delivery of care. This study conducts a comprehensive examination of feature selection methodologies, model architectures, and fine-tuning techniques related to diverse diagnostic and prognostic scenarios within the domain of heart health. It addresses some key gaps in earlier research, including the lack of agreement on which data sources are most effective for classifying stroke and heart attack. This review contributes an analysis of current machine learning methods in stroke and heart attack research, highlighting key gaps such as limited use of multimodal data, external validation, and class imbalance mitigation. It suggests improvements, including the adoption of advanced sampling techniques and the use of comprehensive performance metrics. The findings suggest that despite extensive research on machine learning in cardiovascular health, there are gaps to be addressed in methodologies for data collection, preprocessing, model development, evaluation, and feature engineering.https://doi.org/10.1186/s12938-025-01430-4Cardiovascular healthClassificationData preprocessingDeep learningImage processingHeart attack |
| spellingShingle | Ava Perrone Taghi M. Khoshgoftaar A review of machine learning applications in heart health BioMedical Engineering OnLine Cardiovascular health Classification Data preprocessing Deep learning Image processing Heart attack |
| title | A review of machine learning applications in heart health |
| title_full | A review of machine learning applications in heart health |
| title_fullStr | A review of machine learning applications in heart health |
| title_full_unstemmed | A review of machine learning applications in heart health |
| title_short | A review of machine learning applications in heart health |
| title_sort | review of machine learning applications in heart health |
| topic | Cardiovascular health Classification Data preprocessing Deep learning Image processing Heart attack |
| url | https://doi.org/10.1186/s12938-025-01430-4 |
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