Explainable AI in Action: Health Risk Prediction with LangGraph, MCP, and SHAP.

Author(s): Vikram Bhat

Originally published on Towards AI.

Step-by-Step Guide: Turn a Scikit-learn Model into a Fully Explainable AI Chatbot Using MCP, SHAP, and LangGraph Agents in Streamlit.

In modern AI systems, machine learning models alone aren’t enough. You also need ways to:

This article explores the integration of machine learning models into interactive AI systems, enabling explainability and modularity. It introduces the Model Context Protocol (MCP), showcasing how to wrap a diabetes risk prediction model with SHAP-based explanations, serve it via FastMCP, and employ LangGraph for enhanced interactivity. The resulting architecture allows for real-time chatbot interactions that offer predictions alongside detailed explanations of the contributing factors, emphasizing the importance of transparency in healthcare AI systems.

Read the full blog for free on Medium.

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Published via Towards AI