An Interpretable Approach with Explainable AI for Heart Stroke Prediction.

ABSTRACT: Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model's com

SUBMITTER: Srinivasu PN 

PROVIDER: S-EPMC10813874 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature