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Comprehensive testing scenarios designed to validate different aspects of enhanced medical decision tree functionality using the tree test datasets. Each scenario targets specific analysis capabilities, visualization types, and statistical methods for comprehensive validation.

Usage

tree_test_scenarios

Format

A data frame with 12 observations and 5 variables:

Scenario

Character. Name of the testing scenario

Dataset

Character. Recommended dataset for this scenario

Analysis_Type

Character. Type of analysis being validated

Variables

Character. Key variables for the analysis

Expected_Result

Character. Expected outcome and validation criteria

Source

Generated by create_tree_test_data.R

Details

This documentation provides systematic testing scenarios covering all major medical decision tree analysis capabilities and validation requirements:

Core Analysis Types Tested:

  • Basic Classification: Standard decision tree diagnosis

  • Biomarker Selection: Feature importance and ranking

  • Risk Stratification: Clinical risk group assignment

  • Spatial Analysis: Autocart spatial decision trees

  • Cross-Validation: k-fold performance assessment

  • Bootstrap Validation: Confidence interval estimation

  • Model Comparison: Algorithm performance comparison

  • Clinical Interpretation: Medical guideline generation

  • Missing Data Handling: Imputation and robustness

  • Class Imbalance: Rare disease and outcome handling

  • Feature Scaling: Multi-scale biomarker integration

  • Edge Case Testing: Small sample robustness

Statistical Methods Validated:

  • FFTrees fast-and-frugal trees

  • Spatial autocorrelation analysis

  • Bootstrap confidence intervals

  • Cross-validation performance metrics

  • Clinical performance assessment

  • Risk stratification analysis

Clinical Applications Tested:

  • Oncology diagnosis and staging

  • Cardiovascular risk assessment

  • Pathology pattern recognition

  • Pharmacogenomics treatment selection

  • Pediatric developmental screening

  • Edge case handling and robustness

Each scenario includes the recommended dataset, analysis type, key variables, and expected results for comprehensive validation of enhanced medical decision tree analysis capabilities.

See also