Comparison of Sensitivity, Specificity, and Negative Likelihood Ratio with Deep Learning Models. Metrics are reported as point estimates with 95% CIs. This analysis extends the performance comparison to metrics paramount for clinical application. The proposed model achieves a competitive balance of these metrics against deep learning benchmarks. It attains the highest specificity (98.7%), minimizing false alarms, and a low Negative Likelihood Ratio (0.036), which is comparable to the best deep learning results. This demonstrates that the hybrid framework provides a highly reliable and efficient tool for clinical decision-making.
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https://figshare.com/articles/dataset/Comparison_of_Sensitivity_Specificity_and_Negative_Likelihood_Ratio_with_Deep_Learning_Models_Metrics_are_reported_as_point_estimates_with_95_CIs_This_analysis_extends_the_performance_comparison_to_metrics_paramount_for_clinical_application/30482425
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Comparison of Sensitivity, Specificity, and Negative Likelihood Ratio with Deep Learning Models. Metrics are reported as point estimates with 95% CIs. This analysis extends the performance comparison to metrics paramount for clinical application. The proposed model achieves a competitive balance of these metrics against deep learning benchmarks. It attains the highest specificity (98.7%), minimizing false alarms, and a low Negative Likelihood Ratio (0.036), which is comparable to the best deep learning results. This demonstrates that the hybrid framework provides a highly reliable and efficient tool for clinical decision-making.
创建时间：
2025-10-29
