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Title: :  Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients
PaperId: :  27084
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 4 2025
DUI:    16.0415/IJARIIE-27084
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Anand HadapadCMR University
Jayanthi M Assistant Professor CMR University

Abstract

Computer Science and Engineering
Atrial Fibrillation (AF), Stroke Risk Prediction, Electrocardiogram (ECG), RR Intervals, Heart Rate Variability (HRV), Machine Learning (ML), XGBoost, time-domain features, multimodal data analysis, signal processing, artificial neural network (ANN), SMOTE, Feature Importance, Clinical Decision Support.
Atrial Fibrillation (AF) is a prevalent cardiac arrhythmia that significantly increases the risk of stroke and other cardiovascular complications. Early and accurate detection of AF is critical for timely intervention, yet traditional methods rely heavily on manual analysis of electrocardiogram (ECG) signals, which is time-consuming and prone to error. This study presents an interpretable, multimodal machine learning approach for detecting AF episodes and enabling early stroke risk prediction based on heart rate variability (HRV) features derived from ECG signals.The dataset, sourced from the Erasmus Medical Centre, includes ECG recordings of 804 post-operative patients. RR intervals were extracted using a semi-automatic pipeline and converted into 30-second segments with binary AF labels. A comprehensive feature engineering pipeline generated 30 HRV features spanning the time domain, frequency domain, geometrical, non-linear (CSI and CVI), and Poincaré plot representations. Various machine learning models—including Logistic Regression, Naïve Bayes, KNN-DTW, Random Forest, Artificial Neural Networks (ANN), and XGBoost—were trained using these features.Among all models, the XGBoost classifier demonstrated the highest performance with 99% accuracy and 0.99 recall on the test set. Interpretability was achieved through feature importance analysis, which revealed that time-domain features such as std_hr, pnni_20, and median_nni were the most predictive.This study highlights the potential of interpretable machine learning models for automatic AF detection and sets the stage for future work involving stroke risk prediction by integrating multimodal data, such as clinical parameters and patient demographics.

Citations

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IJARIIE Anand Hadapad, and Jayanthi M . "Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 4 2025 Page 581-588
MLA Anand Hadapad, and Jayanthi M . "Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients." International Journal Of Advance Research And Innovative Ideas In Education 11.4(2025) : 581-588.
APA Anand Hadapad, & Jayanthi M . (2025). Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients. International Journal Of Advance Research And Innovative Ideas In Education, 11(4), 581-588.
Chicago Anand Hadapad, and Jayanthi M . "Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 4 (2025) : 581-588.
Oxford Anand Hadapad, and Jayanthi M . 'Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 4, 2025, p. 581-588. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Interpretable_Multimodal_Machine_Learning_for_Early_Stroke_Risk‬_‭_Prediction_in_Atrial_Fibrillation_Patients_ijariie27084.pdf (Accessed : ).
Harvard Anand Hadapad, and Jayanthi M . (2025) 'Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients', International Journal Of Advance Research And Innovative Ideas In Education, 11(4), pp. 581-588IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Interpretable_Multimodal_Machine_Learning_for_Early_Stroke_Risk‬_‭_Prediction_in_Atrial_Fibrillation_Patients_ijariie27084.pdf (Accessed : )
IEEE Anand Hadapad, and Jayanthi M , "Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 4, pp. 581-588, Jul-Aug 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Interpretable_Multimodal_Machine_Learning_for_Early_Stroke_Risk‬_‭_Prediction_in_Atrial_Fibrillation_Patients_ijariie27084.pdf [Accessed : ].
Turabian Anand Hadapad, and Jayanthi M . "Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 4 ().
Vancouver Anand Hadapad, and Jayanthi M . Interpretable Multimodal Machine Learning for Early Stroke Risk‬ ‭ Prediction in Atrial Fibrillation Patients. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(4) : 581-588. Available from: https://ijariie.com/AdminUploadPdf/Interpretable_Multimodal_Machine_Learning_for_Early_Stroke_Risk‬_‭_Prediction_in_Atrial_Fibrillation_Patients_ijariie27084.pdf
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