This project aims to predict whether a health insurance claim will be approved or not, using machine learning techniques and SHAP-based model interpretability.

Insurance companies process thousands of health insurance claims. Identifying potentially fraudulent or rejected claims early can reduce losses and improve operational efficiency.

This project predicts whether a claim will be approved (1) or rejected (0) based on various features like patient age, diagnosis code, claim amount, procedure details, etc.

• File: enhanced_health_insurance_claims.csv
• Columns Used:
  • ClaimID, ClaimDate, ClaimAmount
  • PatientAge, PatientGender, DiagnosisCode, ProcedureCode
  • ClaimType, ClaimSubmissionMethod, etc.
  • ClaimApproved (Target variable: 1 = Approved, 0 = Rejected)

1. Load Dataset
2. Handle Missing Values
3. Convert Dates & Feature Engineering
4. Encode Categorical Variables
5. Split Dataset (Train/Test)
6. Train XGBoost Classifier
7. Model Evaluation (Accuracy, Report)
8. SHAP Analysis (Explainability)

• Model Used: XGBoost Classifier
• Accuracy: ~46.33%
• Top Features: ClaimAmount, PatientAge, DiagnosisCode_X, etc.
• Explainability: SHAP summary plot included