Comparison of the Predicting Performance for Fate of Medial Meniscus Posterior Root Tear Based on Treatment Strategies: A Comparison between Logistic Regression, Gradient Boosting, and CNN Algorithms
Jae-Ik Lee; Dong-Hyun Kim; Hyun-Jin Yoo; Han-Gyeol Choi; Yong-Seuk Lee · 2021 · Diagnostics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&DL and logistic regression methods. Affecting factors of the logistic regression method co
Abstract by Jae-Ik Lee; Dong-Hyun Kim; Hyun-Jin Yoo; Han-Gyeol Choi; Yong-Seuk Lee, Diagnostics (2021) — licensed CC BY 4.0.
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Metadata source: DOAJ · DOI 10.3390/diagnostics11071225
