Abstract

Diabetic peripheral neuropathy is the most common complication of diabetes, generating physical consequences and limitations in diabetic population. Early diagnosis of this condition based on posture-related tests is still a research issue in the human body movement field. Currently, there is a need for affordable and reliable tools to identify and monitor the progression of this condition. The main goal of this work is to provide a method based on posture feature extraction from the center of pressure time series data, data analysis and machine learning models, in order to find patterns that allow the discrimination among healthy, diabetic and neuropathic people. Time series data from participants are captured using a low cost Wii Balance Board platform, following an experimental protocol. Following a data processing process, that includes reduction of noise, generation of new features, and data augmentation using a method inspired in genetic algorithms to overcome the small number of data. A hierarchical approached is applied in order to deal with class values overlapping. Machine learning algorithms are applied to generate classification models. Results show a range of 89%-97% F1-score, according the case being considered, of the feed-forward neural network models, after the application of the hierarchical approach, being superior to the other classifiers. The high predictive score achieved provides empirical evidence that the proposed approach, based on data augmentation, hierarchical classification and classical machine learning models, can reliably support the detection of diabetic neuropathy condition, also suggesting a simplification of the current procedure.