Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction
The early detection and accurate diagnosis of cardiovascular diseases is vital to reduce global morbidity and death rates. In this work, the quantum-inspired seagull optimization algorithm (QISOA) combined with a deep belief network (DBN) is proposed to improve the identification of cardiovascular d...
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PeerJ Inc.
2024-12-01
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| author | D. Banumathy T. Vetriselvi K. Venkatachalam Jaehyuk Cho |
| author_facet | D. Banumathy T. Vetriselvi K. Venkatachalam Jaehyuk Cho |
| author_sort | D. Banumathy |
| collection | DOAJ |
| description | The early detection and accurate diagnosis of cardiovascular diseases is vital to reduce global morbidity and death rates. In this work, the quantum-inspired seagull optimization algorithm (QISOA) combined with a deep belief network (DBN) is proposed to improve the identification of cardiovascular disorders. As part of preprocessing, cleaning, transformation, and standardization are performed to eliminate noise, inconsistencies, and scaling issues in the data. QISOA is used to optimize the weights and biases of the DBN model, enhancing its prediction efficiency. The algorithm incorporates quantum mechanics concepts to develop its exploration potential further, leading to faster convergence and increased global search efficiency. Optimized DBN provides efficient acquisition of hierarchical representations of the data, which results in improved feature learning and classification accuracy. The publicly accessible Cleveland Heart Disease dataset is used to assess the performance of the suggested model. Extensive experiments are conducted to demonstrate the superior performance of the QISOA-optimized DBN model compared to traditional machine learning and other metaheuristic-based models. Initially, machine learning models such as support vector machines, decision trees, Random Forests, multi-layer perceptrons, and fully connected networks were considered for comparison with the cardiovascular predictive performance of the DBN model. Further, meta-heuristic optimization algorithms such as particle swarm optimization, genetic algorithm, grey wolf optimization, cuckoo search optimization and crow search algorithm are combined with the machine learning models and the classification efficiency is evaluated. Additionally, few state-of-the-art techniques proposed in the existing literature are investigated and compared against the proposed model. It was evident from the comprehensive performance assessment of the proposed model that it yields a higher accuracy of 98.6% with precision, recall, and F1-scores of 97.6%, 96.8%, and 97.1%, respectively, compared to other traditional and existing models for cardiovascular disease prediction. |
| format | Article |
| id | doaj-art-4fe40412d53e4bffa2aa7a72d9dfb95e |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-4fe40412d53e4bffa2aa7a72d9dfb95e2024-12-15T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e249810.7717/peerj-cs.2498Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease predictionD. Banumathy0T. Vetriselvi1K. Venkatachalam2Jaehyuk Cho3Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, IndiaSchool of Computer science and Engineering, VIT University, Vellore, IndiaDepartment of Software Engineering, Jeonbuk National University, Jeonju-si, Republic of South KoreaDepartment of Software Engineering & Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, Republic of South KoreaThe early detection and accurate diagnosis of cardiovascular diseases is vital to reduce global morbidity and death rates. In this work, the quantum-inspired seagull optimization algorithm (QISOA) combined with a deep belief network (DBN) is proposed to improve the identification of cardiovascular disorders. As part of preprocessing, cleaning, transformation, and standardization are performed to eliminate noise, inconsistencies, and scaling issues in the data. QISOA is used to optimize the weights and biases of the DBN model, enhancing its prediction efficiency. The algorithm incorporates quantum mechanics concepts to develop its exploration potential further, leading to faster convergence and increased global search efficiency. Optimized DBN provides efficient acquisition of hierarchical representations of the data, which results in improved feature learning and classification accuracy. The publicly accessible Cleveland Heart Disease dataset is used to assess the performance of the suggested model. Extensive experiments are conducted to demonstrate the superior performance of the QISOA-optimized DBN model compared to traditional machine learning and other metaheuristic-based models. Initially, machine learning models such as support vector machines, decision trees, Random Forests, multi-layer perceptrons, and fully connected networks were considered for comparison with the cardiovascular predictive performance of the DBN model. Further, meta-heuristic optimization algorithms such as particle swarm optimization, genetic algorithm, grey wolf optimization, cuckoo search optimization and crow search algorithm are combined with the machine learning models and the classification efficiency is evaluated. Additionally, few state-of-the-art techniques proposed in the existing literature are investigated and compared against the proposed model. It was evident from the comprehensive performance assessment of the proposed model that it yields a higher accuracy of 98.6% with precision, recall, and F1-scores of 97.6%, 96.8%, and 97.1%, respectively, compared to other traditional and existing models for cardiovascular disease prediction.https://peerj.com/articles/cs-2498.pdfQuantum computingDeep belief networksSeagull optimizationHeart disease predictionDeep learning |
| spellingShingle | D. Banumathy T. Vetriselvi K. Venkatachalam Jaehyuk Cho Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction PeerJ Computer Science Quantum computing Deep belief networks Seagull optimization Heart disease prediction Deep learning |
| title | Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction |
| title_full | Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction |
| title_fullStr | Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction |
| title_full_unstemmed | Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction |
| title_short | Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction |
| title_sort | quantum inspired seagull optimised deep belief network approach for cardiovascular disease prediction |
| topic | Quantum computing Deep belief networks Seagull optimization Heart disease prediction Deep learning |
| url | https://peerj.com/articles/cs-2498.pdf |
| work_keys_str_mv | AT dbanumathy quantuminspiredseagulloptimiseddeepbeliefnetworkapproachforcardiovasculardiseaseprediction AT tvetriselvi quantuminspiredseagulloptimiseddeepbeliefnetworkapproachforcardiovasculardiseaseprediction AT kvenkatachalam quantuminspiredseagulloptimiseddeepbeliefnetworkapproachforcardiovasculardiseaseprediction AT jaehyukcho quantuminspiredseagulloptimiseddeepbeliefnetworkapproachforcardiovasculardiseaseprediction |