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Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A

Marc Ghanem^1,2,3, Abdul Karim Ghaith^1,2, Victor Gabriel El-Hajj^1,2,4

¹Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA.

|December 23, 2023

Summary

This review evaluates machine learning (ML) and deep learning (DL) models for spine surgery, focusing on their performance metrics. It highlights common evaluation errors and biases to improve clinical model reliability.

Keywords:

artificial intelligence deep learning machine learning predictive modeling spine surgery

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Area of Science:

* Spine surgery research
* Machine learning and deep learning applications

Background:

* Increasing use of ML/DL in spine surgery prediction models.
* Need for robust evaluation of models predicting uncommon outcomes.

Purpose of the Study:

* Systematically review and evaluate ML/DL models for spine surgery.
* Focus on models predicting binary outcomes and their evaluation metrics.

Main Methods:

* Systematic review of 60 research papers following PRISMA guidelines.
* Analysis of reported evaluation metrics for various spine surgery outcomes (LOS, readmissions, etc.).

Main Results:

* Identified common outcomes predicted: length of stay, readmissions, non-home discharge, mortality, reoperations.
* Observed significant data imbalance (0.44%–42.4%) for target outcomes.
* Wide variation in reported metrics (AUROC, accuracy, sensitivity, specificity, PPV, NPV, Brier score, F1 score) and data visualization.

Conclusions:

* Highlights common errors and potential biases in ML model evaluation for spine surgery.
* Recommends appropriate evaluation schemes to enhance model reliability and clinical integration.
* Emphasizes the need for standardized evaluation to facilitate effective ML model adoption in healthcare.