Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50.
Authors
Affiliations (8)
Affiliations (8)
- Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Chulalongkorn University, Bangkok, 10330, Thailand.
- Division of Neurology, Department of Medicine, Faculty of Medicine, Blekinge Institute of Technology, Blekinge, Sweden.
- Division of Neurology, Department of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand. [email protected].
- Chulalongkorn Stroke Center, Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand. [email protected].
- Division of Neurology, Department of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
- Chulalongkorn Stroke Center, Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand.
- Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Chulalongkorn University, Bangkok, 10330, Thailand. [email protected].
- Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Chulalongkorn University, Bangkok, 10330, Thailand. [email protected].
Abstract
Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the serious health consequences and economic burden that strokes can cause. In this study, we proposed a class-balanced and data-augmented (CBDA-ResNet50) deep learning model to improve the prediction accuracy of the well-known ResNet50 architecture for stroke risk. Our approach uses advanced techniques such as class balancing and data augmentation to address common challenges in medical imaging datasets, such as class imbalance and limited training examples. In most cases, these problems lead to biased or less reliable predictions. To address these issues, the proposed model assures that the predictions are still accurate even when some stroke risk factors are absent in the data. The performance of CBDA-ResNet50 improves by using the Adam optimizer and the ReduceLROnPlateau scheduler to adjust the learning rate. The application of weighted cross entropy removes the imbalance between classes and significantly improves the results. It achieves an accuracy of 97.87% and a balanced accuracy of 98.27%, better than many of the previous best models. This shows that we can make more reliable predictions by combining modern deep-learning models with advanced data-processing techniques. CBDA-ResNet50 has the potential to be a model for early stroke prevention, aiming to improve patient outcomes and reduce healthcare costs.