An Intuitive Approach on Transfer Learning with an IBF+IHP Model for Stroke Classification and Prediction
Received: 18 September 2024 | Revised: 21 October 2024 | Accepted: 27 November 2024 | Online: 2 February 2025
Corresponding author: Talekar Rohini
Abstract
A cerebral stroke can have significant health ramifications. Efficient stroke prevention requires precise prevention and prompt detection of risk factors. This study introduces a novel predictive modeling technique that uses uncomplicated spatial filter maps and ensemble approaches to enhance stroke risk prediction. The proposed approach utilizes ensemble approaches along with comprehensible spatial filter maps to uncover significant spatial patterns in brain imaging data. The ensemble approach employs a multitude of prediction models to enhance the accuracy of stroke risk forecasts. The experimental findings demonstrate that spatial filter maps and ensemble techniques surpass traditional models in predicting performance. This study showcases the potential of spatial filters to include several patient data to accurately predict stroke risk with a 98% success rate.
Keywords:
stroke prediction, ensemble models, spatial filter mapsDownloads
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Copyright (c) 2024 Talekar Rohini, P. Praveen
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