Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Visvanathan, P. | Durai Raj Vincent, P.M.; *
Affiliations: School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Correspondence: [*] Corresponding author. P.M. Durai Raj Vincent, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India. E-mail: [email protected].
Abstract: A Stroke is a sudden loss of blood circulation in certain parts of the brain that results in a loss of neurological function. To save a patient from stroke, an immediate diagnosis and treatment plan must be implemented. Artificial intelligence-based machine learning algorithms play a major role in the prediction. To predict a person likely to have a stroke, stroke healthcare data records must be accessed, which is very sensitive. Data shared for machine learning training pose security risks and have concerns about privacy. To overcome this issue, Genetic Algorithm and Federated Learning (GA-FL) –based hybridization approach is proposed to predict the risk of stroke in a person. Federated Learning was developed by Google, which can provide security to the data during the training process because every client participating in this training process needs to exchange only the training parameters without sharing the data. In addition to the security features, a genetic algorithm was used to optimize the parameters required to train a model using the perceptron neural network model. The experimental results show that our proposed research model (GA-FL) provides security and predicts the risk of stroke more accurately than any other existing algorithm.
Keywords: Federated learning, genetic algorithm, stroke risk, perceptron neural network
DOI: 10.3233/JIFS-236354
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]