Diagnosis of neuropathies in diabetic patients by applying machine learning

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 (CRISP-DM) guide. The main objective of the research is to build a model for classifying diabetic neuropathy in patients using data recorded by the expert and mainly by using a Wii Balance Board to capture postural variations during clinical evaluations. The results obtained in each phase of the process are analyzed and performance metrics for the machine learning models are evaluated.