Dimensionality Reduction of Data on Patients with Diabetes Mellitus by Multidimensional Scaling

Abstract

Diabetes Mellitus is a global public health problem. According to the World Health Organization, more than 6% of the world's population suffers from diabetes. In the context of the Russian invasion, the problem of diabetes is especially relevant for Ukraine. This is due to the difficulty of supplying medicines and obtaining medical care. Also, the stress caused by the war is one of the factors in the appearance and complications of diabetes. Automated models and information technologies for classifying patients with suspected diseases are practical decision support tools for making medical diagnoses in resource-limited settings. One of the problems with using such models is data redundancy. Therefore, this study uses multidimensional scaling to focus on dimensionality reduction in patients with suspected Diabetes Mellitus type II.