A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization

<b>Background:</b> Lumbar disc herniation (LDH) recurrence remains a pressing clinical challenge, with limited predictive tools available to support early identification and personalized intervention. Predicting recurrence after lumbar disc herniation (LDH) remains clinically important b...

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Bibliographic Details
Main Authors:	Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu
Format:	Article
Language:	English
Published:	MDPI AG 2025-06-01
Series:	Diagnostics
Subjects:	lumbar disc herniation recurrence prediction ensemble learning autoencoder threshold tuning class imbalance
Online Access:	https://www.mdpi.com/2075-4418/15/13/1628
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization

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