Explainable Deep Temporal Modeling for Stroke Risk Assessment Using Attention-Based LSTM Networks

Abstract: Stroke continues to be a major cause of mortality and disability globally, and precise risk prediction models are needed. Current models do not effectively incorporate temporal patient information, restricting the quality of prediction and clinical interpretability. This research introduces a new LSTM-based deep learning model enriched with an attention mechanism for predicting stroke risk that can prioritize important risk factors like age, hypertension, and heart disease. The model takes advantage of LSTM's ability to learn sequential dependencies from long-term patient histories, while the attention mechanism dynamically emphasizes clinically important features, promoting interpretability and clinical significance. By testing the model using a dataset of 5,110 patient records with a mere 6% stroke cases, showcasing extreme class imbalance. To counteract this, preprocessing involved SMOTE for synthetic oversampling, mean imputation to handle missing values, and Min-Max normalization. As deployed in Python based on TensorFlow, the model realized remarkable performance. The constructed LSTM-Attention model attained a test accuracy of 83.7%, an AUC-ROC value of 85.3%, and an F1-value of 82.2%, which was higher than that of conventional models such as Logistic Regression and Random Forest. These evaluate the model's improved ability to identify subtle stroke risk factors that go unnoticed otherwise. The attention-augmented LSTM architecture not only guarantees accurate predictions but also offers transparent insight into the decision process, making it appropriate for incorporation in real-time clinical decision support systems. This method has the potential to improve personalized stroke risk assessment dramatically and enhance preventive healthcare interventions.

P. Selvaperumal, F. Sheeja Mary, Pratik Gite, T L Deepika Roy, Yousef A. Baker El-Ebiary, Gowrisankar Kalakoti and Sandeep Kumar Mathariya. “Explainable Deep Temporal Modeling for Stroke Risk Assessment Using Attention-Based LSTM Networks”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160667

@article{Selvaperumal2025,
title = {Explainable Deep Temporal Modeling for Stroke Risk Assessment Using Attention-Based LSTM Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160667},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160667},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {P. Selvaperumal and F. Sheeja Mary and Pratik Gite and T L Deepika Roy and Yousef A. Baker El-Ebiary and Gowrisankar Kalakoti and Sandeep Kumar Mathariya}
}