- Title
- An intelligent heart disease prediction system based on swarm-artificial neural network
- Creator
- Nandy, Sudarshan; Adhikari, Mainak; Balasubramanian, Venki; Menon, Varun; Li, Xingwang; Zakarya, Muhammad
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194013
- Identifier
- vital:18306
- Identifier
-
https://doi.org/10.1007/s00521-021-06124-1
- Identifier
- ISSN:0941-0643 (ISSN)
- Abstract
- The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Neural Computing and Applications Vol. 35, no. 20 (2023), p. 14723-14737
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021, The Author(s)
- Subject
- 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; 4611 Machine learning; Artificial neural network; Back-propagation; Classification model; Heart disease prediction; Heuristic formulation; Swarm optimization
- Reviewed
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