Handwriting-Based Classification of Hepatic Encephalopathy Using Nonlinear Complexity Features

Hepatic encephalopathy (HE) is a serious disease in cirrhotic subjects with unclear pathogenesis and no standardized diagnostic method. As HE affects brain function, it can affect handwriting, demanding coordination, and cognitive task. This study explores the potential correlation between HE, liver cirrhosis, and handwriting. The handwriting data were captured using a graphic pen and tablet, with which the subjects performed eight writing tasks and two drawing tasks. A total of 1965 basic and advanced features from both surface and in-air movements were obtained from these data. Several experiments were performed to analyze the impact of different feature groups on the prediction results. In this study, the weighted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-nearest neighbors feature selection (WkNN-FS) was used. The final XGBoost model was trained using the selected set of 300 features, and achieved an accuracy of 81.96% (95% CI: 79.32-91.62) and an AUROC of 87.31% (95% CI: 78.96-95.66).