Hybrid Deep Transfer Learning Approach for Predicting Risk of Stroke

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Published: May 29, 2023
Keywords:
Bayesian optimization transfer learning generative adversarial networks stroke risk prediction active learning

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M. Pragathi
Assistant Professor, Computer Science & Engineering, Sridevi Women’s Engineering College, Hyderabad, India
B. Pavithra
Computer Science & Engineering, Sridevi Women’s Engineering College, B. Tech IV Year, Hyderabad, India
D. Likitha
Computer Science & Engineering, Sridevi Women’s Engineering College, B. Tech IV Year, Hyderabad, India
M. Kalpana
Computer Science & Engineering, Sridevi Women’s Engineering College, B. Tech IV Year, Hyderabad, India

Abstract

Internationally, stroke is the main source of death and long haul incapacity, and there is presently no compelling treatment. Deep learning-based calculations perform better compared to current stroke risk forecast frameworks, yet they need a ton of exactly named information. As an outcome of thorough protection regulations in medical services frameworks, stroke information is regularly traded in pieces around different associations. The information's positive and negative models are comparably considerably one-sided. Transfer learning can help with minor information challenges by utilizing knowledge from a similar field when there are multiple information sources available. This article proposes a guile Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) approach for dealing with the data plan of different associated focal points (for example, information on industrious conditions like diabetes and hypertension, as well as outer stroke data). The best stroke risk forecast calculations right now miss the mark concerning the outcomes accomplished by the proposed methodology, which has been thoroughly tried in both fictitious and certifiable situations.

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How to Cite
M. Pragathi, B. Pavithra, D. Likitha, & M. Kalpana. (2023). Hybrid Deep Transfer Learning Approach for Predicting Risk of Stroke. Journal of Coastal Life Medicine, 11(2), 660–667. Retrieved from https://www.jclmm.com/index.php/journal/article/view/1063
Issue
Vol. 11: Number 2, 2023
Section
Articles

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