An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms
Heart attacks are among the leading causes of death globally, and the earliest possible identification of at-risk patients is critical to lowering deaths. Advanced machine learning and deep learning algorithms have been effectively used to predict the presence of heart attack based on clinical and l...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442525000267 |
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| author | Maithri Bairy Krishnaraj Chadaga Niranjana Sampathila R. Vijaya Arjunan G. Muralidhar Bairy |
| author_facet | Maithri Bairy Krishnaraj Chadaga Niranjana Sampathila R. Vijaya Arjunan G. Muralidhar Bairy |
| author_sort | Maithri Bairy |
| collection | DOAJ |
| description | Heart attacks are among the leading causes of death globally, and the earliest possible identification of at-risk patients is critical to lowering deaths. Advanced machine learning and deep learning algorithms have been effectively used to predict the presence of heart attack based on clinical and laboratory markers. This study used five explainable artificial intelligence techniques (XAI) to ensure that predictions made by the model are understandable and interpretable to facilitate clinical decisions. Fourteen nature-inspired feature selection algorithms were applied to identify the most informative markers while optimizing the predictive models for greater accuracy and reliability. Mutual information achieved a maximum testing accuracy of 90 % and highest precision of 94 %. The Whale Optimization Algorithm, Jaya Algorithm, Grey Wolf Optimizer and Sine Cosine Algorithm were the next best performing algorithms. The XAI results showed that the most important markers were ST slope, Oldpeak, exercise-induced angina, chest pain type, and fasting blood sugar. These models can be implemented in healthcare institutions to predict heart attack risks early, allowing timely interventions to reduce the likelihood of severe cardiovascular diseases. By supporting healthcare professionals with computer-aided diagnostic tools, these systems can enhance patient-specific decision-making while alleviating strain on healthcare resources. |
| format | Article |
| id | doaj-art-07c806f33b1946bb8b3de6f1be6437a8 |
| institution | DOAJ |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-07c806f33b1946bb8b3de6f1be6437a82025-08-20T02:40:47ZengElsevierHealthcare Analytics2772-44252025-12-01810040710.1016/j.health.2025.100407An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithmsMaithri Bairy0Krishnaraj Chadaga1Niranjana Sampathila2R. Vijaya Arjunan3G. Muralidhar Bairy4Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India; Corresponding author.Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaHeart attacks are among the leading causes of death globally, and the earliest possible identification of at-risk patients is critical to lowering deaths. Advanced machine learning and deep learning algorithms have been effectively used to predict the presence of heart attack based on clinical and laboratory markers. This study used five explainable artificial intelligence techniques (XAI) to ensure that predictions made by the model are understandable and interpretable to facilitate clinical decisions. Fourteen nature-inspired feature selection algorithms were applied to identify the most informative markers while optimizing the predictive models for greater accuracy and reliability. Mutual information achieved a maximum testing accuracy of 90 % and highest precision of 94 %. The Whale Optimization Algorithm, Jaya Algorithm, Grey Wolf Optimizer and Sine Cosine Algorithm were the next best performing algorithms. The XAI results showed that the most important markers were ST slope, Oldpeak, exercise-induced angina, chest pain type, and fasting blood sugar. These models can be implemented in healthcare institutions to predict heart attack risks early, allowing timely interventions to reduce the likelihood of severe cardiovascular diseases. By supporting healthcare professionals with computer-aided diagnostic tools, these systems can enhance patient-specific decision-making while alleviating strain on healthcare resources.http://www.sciencedirect.com/science/article/pii/S2772442525000267Heart attack predictionExplainable machine learningFeature selection analyticsPredictive healthcare modelingData-driven risk assessmentComputational clinical insights |
| spellingShingle | Maithri Bairy Krishnaraj Chadaga Niranjana Sampathila R. Vijaya Arjunan G. Muralidhar Bairy An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms Healthcare Analytics Heart attack prediction Explainable machine learning Feature selection analytics Predictive healthcare modeling Data-driven risk assessment Computational clinical insights |
| title | An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms |
| title_full | An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms |
| title_fullStr | An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms |
| title_full_unstemmed | An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms |
| title_short | An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms |
| title_sort | explainable analytical approach to heart attack detection using biomarkers and nature inspired algorithms |
| topic | Heart attack prediction Explainable machine learning Feature selection analytics Predictive healthcare modeling Data-driven risk assessment Computational clinical insights |
| url | http://www.sciencedirect.com/science/article/pii/S2772442525000267 |
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