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This is a Quantitative Study study.

October 1, 2025Open Access

Model uncertainty quantification: A post hoc calibration approach for heart disease prediction

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Isotonic calibration consistently improved model probability reliability while maintaining discrimination.
Baseline accuracy metrics were high, with SVM reaching an accuracy of 92.9% and ROC-AUC of 99.4%.
Post hoc calibration was evaluated using Brier score, expected calibration error, log loss, and reliability diagrams.
The results emphasize the importance of calibration methods in enhancing the interpretability of heart disease prediction models.

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Experimental analysis shows that isotonic calibration improves probability quality in heart disease models, suggesting better clinical reliability.

Odesola et al. (2025) studied this question.

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Experimental analysis shows that isotonic calibration improves probability quality in heart disease models, suggesting better clinical reliability.