Using Decision Tree Classifier to Increase Screening Test Sensitivity for the Prediction of ACL Retear

Abstract

Screening tests are often used in medicine to assess whether a patient is at a high risk of contracting a disease. Recent literature has proposed prediction algorithms for Anterior Cruciate Ligament (ACL) retears that aim to achieve high accuracy. However, these models fail to reach an adequate sensitivity to function as effective screening tests. In such cases, model sensitivity is sacrificed for heightened specificity. Misclassifying patients who will eventually go on to retear their ACL as low-risk patients prevents them from obtaining necessary therapeutic support and is not appropriate for a clinical setting. In this study, we implement a Decision Tree Classifier as a screening test to evaluate a patient's risk of retearing their ACL six months after surgery, before the patient is released to activity. By incorporating a machine learning-based screening technique, we hope to minimize false negatives and create a tool that can readily be adopted in clinical practice.

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