Predictive analysis of heart disease using quantum-assisted machine learning
Abstract Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to t...
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| Format: | Article |
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Springer
2025-05-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06944-z |
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| author | Mehroush Banday Sherin Zafar Parul Agarwal M. Afshar Alam Siddhartha Sankar Biswas Imran Hussain K. M. Abubeker |
| author_facet | Mehroush Banday Sherin Zafar Parul Agarwal M. Afshar Alam Siddhartha Sankar Biswas Imran Hussain K. M. Abubeker |
| author_sort | Mehroush Banday |
| collection | DOAJ |
| description | Abstract Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method’s robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilises techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional ML algorithms and to minimise computational expenses. The proposed method has been developed in the Raspberry Pi 4B Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. The proposed system performance obtained an accuracy of 99%, utilising 20000 datasets with 14 attributes from various datasets collected from the Local Pathology Lab in the Muzaffarnagar District of Uttar Pradesh, India. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher. |
| format | Article |
| id | doaj-art-99e5d36994c540b8982125d77df512b7 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-99e5d36994c540b8982125d77df512b72025-08-20T03:52:19ZengSpringerDiscover Applied Sciences3004-92612025-05-017511610.1007/s42452-025-06944-zPredictive analysis of heart disease using quantum-assisted machine learningMehroush Banday0Sherin Zafar1Parul Agarwal2M. Afshar Alam3Siddhartha Sankar Biswas4Imran Hussain5K. M. Abubeker6Department of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyDepartment of Computer Science and Engineering, School of Engineering Sciences and TechnologyAmal Jyothi College of Engineering (Autonomous)Abstract Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method’s robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilises techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional ML algorithms and to minimise computational expenses. The proposed method has been developed in the Raspberry Pi 4B Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. The proposed system performance obtained an accuracy of 99%, utilising 20000 datasets with 14 attributes from various datasets collected from the Local Pathology Lab in the Muzaffarnagar District of Uttar Pradesh, India. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher.https://doi.org/10.1007/s42452-025-06944-zHealthcare 4.0Heart diseaseDeep learningGPUQuantum machine learningQuantum neural networks |
| spellingShingle | Mehroush Banday Sherin Zafar Parul Agarwal M. Afshar Alam Siddhartha Sankar Biswas Imran Hussain K. M. Abubeker Predictive analysis of heart disease using quantum-assisted machine learning Discover Applied Sciences Healthcare 4.0 Heart disease Deep learning GPU Quantum machine learning Quantum neural networks |
| title | Predictive analysis of heart disease using quantum-assisted machine learning |
| title_full | Predictive analysis of heart disease using quantum-assisted machine learning |
| title_fullStr | Predictive analysis of heart disease using quantum-assisted machine learning |
| title_full_unstemmed | Predictive analysis of heart disease using quantum-assisted machine learning |
| title_short | Predictive analysis of heart disease using quantum-assisted machine learning |
| title_sort | predictive analysis of heart disease using quantum assisted machine learning |
| topic | Healthcare 4.0 Heart disease Deep learning GPU Quantum machine learning Quantum neural networks |
| url | https://doi.org/10.1007/s42452-025-06944-z |
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