Abstract

ABSTRACT This article describes the construction of classification models by applying machine learning algorithms, in the kinesiological domain of neuropathy diagnosis, in patients with diabetes. The process is developed using the Cross Industry Standard Process for Data Mining guide (CRISP-DM), according to the general process of Knowledge Discovery in Databases (KDD). The main objective of the research is to build a model for classifying the condition of diabetic neuropathy in patients, using the data recorded by the expert and, mainly, by using a Wii Balance Board to capture the postural variations of a subject undergoing a series of clinical evaluations. In this research, the results obtained in each phase of the process are analyzed and the degree of achievement of objectives and performance metrics in the evaluation of machine learning models are measured. The results obtained will be expanded with the analysis of time series and the extraction of features, under the hypothesis that the center of pressure (COP) is a sufficient and reliable predictor to discriminate against the existence of a neuropathy.