Machine learning for the early prediction of head-up tilt testing outcome

Abstract

ObjectiveHead-up Tilt Testing (HUTT) is a widely used medical tool for the diagnosis of unexplained syncope. Current HUTT protocols, however, are time-consuming. This study aims to investigate the feasibility of using hemodynamic monitoring and machine learning techniques to achieve early prediction of HUTT outcome for syncope patients. MethodsA total of 209 subjects participated in this study from June 2016 to November 2019, and hemodynamic signals were collected via a Finometer device (Finapres Medical Systems BV, The Netherlands). We extracted features with a total dimension of 4,313 from the early 18 min (5 min of supine position and 13 min of tilting position). A genetic algorithm (GA) was introduced for feature selection, and an index called the selection ratio (SR) was proposed to further analyze the GA selection result. Four machine learning models were established for this classification task, and their performance results were compared. ResultsThe maximum tilting duration was shortened from 35 min to 13 min, and a best area under receiver operating characteristic curve of 0.94 via 5-fold cross-validation was obtained by the SVR model, with a sensitivity of 0.86 and a specificity of 0.82. The performance of all algorithms improved after feature selection by GA. ConclusionThe proposed approach is a promising method to shorten the diagnosis time compared to the existing diagnosis process. The GA introduced in this study is an effective feature selection tool to improve model performance. The proposed SR index effectively contributes to the usability and interpretability of the model.