Machine Learning-based detection of long COVID using Heart Rate Variability Analysis
arXiv:2607.13668
The study uses machine learning on heart rate variability measurements to detect long COVID, achieving up to 85% accuracy with a gradient boosting model.
Abstract
After COVID epidemic has ravaged the world, around 20% of infected subjects continue to manifest symptoms several months after their cure. This disorder is called long COVID. This paper presents a study carried out at the University Hospital of Ourense with the aim of establishing a relationship among the disease and variations in Heart rate variability (HRV) parameters using machine learning (ML). Five heart rate recordings were obtained per subject, both at rest and under conditions of physical effort and stress. Each record was processed and 15 HRV indices were extracted, giving 75 features per patient. Of these features, 16 were selected to train 10 different ML models: Support Vector Classification, Linear Support Vector Classification, Logistic Regression, Linear Discriminant Analysis, Stochastic Gradient Descent, Multiple Layer Perceptron, Naive Bayes, Random Forest, and Gradient and ADA Boost Classifiers. Results show that the best model, Gradient Boost, achieves an accuracy of 85.2%, F1-score of 84.9%, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.907, and that all models exceed 0.833 AUC. This study demonstrates an association between long COVID and heart rate variability (HRV), highlighting the utility of machine learning models in identifying this relationship and supporting its diagnose.
14 pages, 4 figures and 4 tables