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©The Author(s) 2025.
World J Clin Cases. Aug 6, 2025; 13(22): 104379
Published online Aug 6, 2025. doi: 10.12998/wjcc.v13.i22.104379
Published online Aug 6, 2025. doi: 10.12998/wjcc.v13.i22.104379
Table 2 Results of training and test set metrics of groups based on acute appendicitis status
Groups, metrics (%) | Control vs entire AAp | Uncomplicated vs complicated AAp | ||
Training set | Test set | Training set | Test set | |
Accuracy | 96.5 | 96.3 | 82.5 | 78.9 |
Balanced accuracy | 95.4 | 97.4 | 81.1 | 80.1 |
Sensitivity | 97.5 | 94.7 | 76.9 | 83.3 |
Specificity | 93.9 | 100 | 85.2 | 76.9 |
Positive predictive value | 97.5 | 100 | 71.4 | 62.5 |
Negative predictive value | 93.9 | 88.9 | 88.5 | 90.9 |
F1 score | 97.5 | 97.3 | 74.1 | 71.4 |
AUC | 96.4 | 94.7 | 82.5 | 79.0 |
Accuracy | 96.5 | 96.3 | 82.5 | 78.9 |
- Citation: Kucukakcali Z, Akbulut S. Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model. World J Clin Cases 2025; 13(22): 104379
- URL: https://www.wjgnet.com/2307-8960/full/v13/i22/104379.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v13.i22.104379