IDENTIFICATION AND MITIGATION OF BIAS USING EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) FOR BRAIN STROKE PREDICTION

Abstract

Stroke is a time-sensitive illness that without rapid care and diagnosis can result in detrimental effects on the person. Caretakers need to enhance patient management by procedurally mining and storing the patient's medical records because of the increasing synergy between technology and medical diagnosis. Therefore, it is essential to explore how these risk variables interconnect with each other in patient health records and understand how they each individually affect stroke prediction. Using explainable Artificial Intelligence (XAI) techniques, we were able to show the imbalance dataset and improve our model’s accuracy, we showed how oversampling improves our model’s performance and used explainable AI techniques to further investigate the decision and oversample a feature to have even better performance. We showed and suggested explainable AI as a technique to improve model performance and serve as a level of trustworthiness for practitioners, we used four evaluation metrics, recall, precision, accuracy, and f1 score. The f1 score with the original data was 0% due to imbalanced data, the non-stroke data was significantly higher than the stroke data, the 2nd model has an f1 score of 81.78% and we used explainable AI techniques, Local Interpretable Model-agnostic Explanations (LIME) and SHapely Additive exPlanation (SHAP) to further analyse how the model came to a decision, this led us to investigate and oversample a specific feature to have a new f1 score of 83.34%. We suggest the use of explainable AI as a technique to further investigate a model’s method for decision-making.