A neural network model using heart rate variability parameters accurately diagnosed complete versus incomplete spinal cord injury, achieving 86.67% accuracy and an AUC of 0.9608.
Can machine learning algorithms applied to heart rate variability parameters accurately diagnose complete versus incomplete spinal cord injury?
Machine learning models utilizing heart rate variability and demographic data can accurately differentiate between complete and incomplete spinal cord injury.
Estimación del efecto: AUC 0.9608
Objective: The aim of the study was to develop a model using machine learning algorithms for diagnosing complete and incomplete injury from heart rate variation (HRV) parameters and other easily obtained demographics. Design: Random Forest, Decision Tree, KNN Classifier, Gradient Boost, Bagging Classifier, Support Vector Machine, Voting Classifier, XGB Classifier, MLP Classifier and feedforward neural networks were trained on 296 sets of patient data including 11 HRV parameters. Feature selection methods were used to improve models and identify parameters with high contribution to model predictions. Results: The feature selected MLPClassifier achieved an accuracy of 85.33%, with an AUC of 0.8590 while the neural network model had an accuracy of 86.67% with an AUC of 0.9608. Location of spinal injury, age, mean heart rate and mean R-R interval were the greatest contributors to the machine learning models. Conclusions: This study demonstrated that machine learning algorithms trained on HRV data could be an invaluable tool for diagnosing and monitoring people with SCI and overall improving their quality of life.
Syed et al. (Mon,) conducted a other in Spinal Cord Injury (n=296). Machine learning algorithms using heart rate variability was evaluated on Diagnosis of complete versus incomplete spinal cord injury (AUC 0.9608). A neural network model using heart rate variability parameters accurately diagnosed complete versus incomplete spinal cord injury, achieving 86.67% accuracy and an AUC of 0.9608.