AI Healthcare
Diagnostics.
Advanced diagnostic engine combining Random Forest ML, SHAP Explainability, and Groq LLM — delivering transparent, evidence-based clinical assessments.
Explainable AI.
Every diagnosis comes with complete transparency — real SHAP values, counterfactual what-if scenarios, feature interaction analysis, and a multi-factor trust score.
Random Forest ML
200-tree ensemble classifier trained on clinically-accurate disease profiles from Harrison's Principles and WHO ICD-11.
SHAP Explainability
TreeExplainer computes real Shapley values for each prediction, showing exactly how each symptom influences the diagnosis.
Counterfactual Analysis
The model is re-run with each symptom toggled to show exactly how the diagnosis would change.
Trust Score
4-factor confidence composite combining model certainty, prediction margin, symptom specificity, and cross-validation reliability.
Risk Assessment
Automated clinical risk factors, potential complications, and evidence-based recommendations for each diagnosis.
Groq LLM Narratives
Llama 3.3 70B generates natural language clinical narratives and differential diagnoses via Groq's inference.