Authors: Pradhebaa S
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) models have become powerful tools for predictive analytics in medical and financial domains, enabling early diagnosis of disease, fraud detection, and risk forecasting with remarkable accuracy. Despite these advancements, most state-of-the-art models operate as complex black-box systems, offering minimal transparency into how predictions are formed. In healthcare, where predictions influence clinical decisions, lack of interpretability reduces clinician trust, raises ethical concerns, and limits real-world deployment. Similarly, in finance, opaque ML systems create challenges in regulatory audits, accountability, and fairness in automated risk scoring. These limitations motivate the need for Explainable AI (XAI) frameworks that provide human-interpretable reasoning without sacrificing predictive performance. This paper proposes a unified, model-agnostic explainable machine learning framework tailored for high-stakes prediction tasks. The system employs predictive models such as Random Forest, XGBoost, and LSTM for structured and longitudinal clinical data, integrated with XAI methods including SHAP, LIME, attention visualization, and counterfactual reasoning to generate both global and instance-level explanations. To enhance explanation reliability, the framework incorporates stability analysis, imbalance-aware training, and a composite trust scoring mechanism validated by domain experts. The approach aims to improve transparency, support clinician and analyst decision-making, and enable safer, auditable deployment of AI in medical prediction pipelines. Experimental results from existing research demonstrate that combining high-accuracy ML with robust explanation layers significantly improves stakeholder trust and practical adoption, positioning the framework as a step toward responsible and interpretable predictive intelligence in real-world applications.
Published by: vikaspatanker
