Diagnosis of spinal column pathologies using ensemble with rejection option
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1216Keywords:
Cervical Column, Confidence, Reject OptionAbstract
Objective: To propose a new approach for decision-making with rejection option in classifier committees. Method: The developed method encompasses classification techniques using ensembles with the Rejection Option approach, employing the Gini Index (GI) as a confidence metric. We established thresholds based on the distribution of purity percentages obtained from each class, allowing the model to abstain from predicting samples that are difficult to classify in medical diagnostics related to cervical column diseases. Results: The proposed model outperformed comparisons, achieving 97.55% accuracy and rejecting 61.69% of samples in the most conservative scenario. The Accuracy and Rejection curve highlighted its superiority. Conclusion: Defining GI value ranges offers flexibility in adjusting the committee's rigidity, further revealing potential for optimizing classifier committees in various applications, providing greater reliability in pattern recognition.
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