Predicting Complete Injury in Spinal Cord Injury Patients by Applying Machine Learning Methods to Heart Rate Variability.

Abstract

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.

Methods: 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 MLP Classifier 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.