Filtering instances and rejecting predictions to obtain reliable models in healthcare

• URL: http://arxiv.org/abs/2510.24368v1
• Date: Tue, 28 Oct 2025 12:45:20 GMT
• Title: Filtering instances and rejecting predictions to obtain reliable models in healthcare
• Authors: Maria Gabriela Valeriano, David Kohan Marzagão, Alfredo Montelongo, Carlos Roberto Veiga Kiffer, Natan Katz, Ana Carolina Lorena,
• Abstract summary: We propose a two-step data-centric approach to enhance the performance of Machine Learning models.<n>The first step involves leveraging Instance Hardness (IH) to filter problematic instances during training.<n>The second step introduces a confidence-based rejection mechanism during inference, ensuring that only reliable predictions are retained.
• Score: 0.2524526956420465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Learning (ML) models are widely used in high-stakes domains such as healthcare, where the reliability of predictions is critical. However, these models often fail to account for uncertainty, providing predictions even with low confidence. This work proposes a novel two-step data-centric approach to enhance the performance of ML models by improving data quality and filtering low-confidence predictions. The first step involves leveraging Instance Hardness (IH) to filter problematic instances during training, thereby refining the dataset. The second step introduces a confidence-based rejection mechanism during inference, ensuring that only reliable predictions are retained. We evaluate our approach using three real-world healthcare datasets, demonstrating its effectiveness at improving model reliability while balancing predictive performance and rejection rate. Additionally, we use alternative criteria - influence values for filtering and uncertainty for rejection - as baselines to evaluate the efficiency of the proposed method. The results demonstrate that integrating IH filtering with confidence-based rejection effectively enhances model performance while preserving a large proportion of instances. This approach provides a practical method for deploying ML systems in safety-critical applications.

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