While machine learning models achieve promising results in diabetes prediction, clinical adoption remains limited due to black-box nature and lack of stakeholder-specific communication. This study proposes a novel multi-level explanation framework that translates a single XGBoost prediction (Accuracy: 69.5%, AUC-ROC: 0.76) into three consistent explanations using SHAP: 1. Patient Layer: Non-technical empathetic explanation 2. Clinician Layer: Medical-focused explanation 3. Developer Layer: Technical SHAP visualization Dataset: Pima Indians Diabetes (768 samples) GitHub: https://github.com/kashish-alt0786/Medical-IT-Diabetes-AI-Project Live Demo: https://medical-it-diabetes-ai-project-yv5kg3s5n3mw7eefy9w2mf.streamlit.app/ Keywords: XAI, Diabetes, SHAP, XGBoost
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The accurate identification of fault locations in power transmission networks is critical for ensuring system reliability and reducing downtime. Traditional fault location methods, such as impedance-based techniques, have been widely used, but they often suffer from limitations d…
Radiography, renowned for its diagnostic prowess and affordability, plays a key role in detecting diseases, including critical conditions. Chest radiography, focusing on a vital body area, poses interpretational challenges, necessitating experienced radiologists for accurate diag…