STUDY OF PREDICTION AND CLASSIFICATION MODELS IN THE PROBLEMS OF DIABETES AMONG PATIENTS WITH A STROKE IN DIFFERENT LIVING CONDITIONS

Abstract

The subject of the study in the article is the methods of predicting the development of diabetes. Diabetes mellitus is a non-communicable disease that has affected 425 million people, and by 2045 the number will only increase by 1.5 times. It has been proven to be an independent contributing factor to stroke development. When there is too much sugar in the blood, it negatively affects the arteries and blood vessels. People with this disease are more likely to develop atherosclerotic plaques and blood clots, which can lead to heart blockage and ischemic stroke. Having diabetes increases the risk and worsens the course of a stroke. According to the Framingham Study, the number of recurrent cases doubles. The aim of the study is to investigate methods of predicting and classifying the development of diabetes among people, in particular stroke patients, to prevent the development of other diseases. The complexity of the problem lies in the fact that there are as many undiagnosed cases as diagnosed ones, so about half of people suffer from the disease and the resulting complications due to improper or delayed diagnosis. Therefore, timely diagnosis of a disease that is difficult to detect is important in order to prevent the development of further complications. The article solves the problem of a multi-criteria task of choosing the best algorithm for predicting the occurrence of a disease. The following methods are used in this paper: multilayer perceptron, k-nearest neighbors method, decision tree, and logistic regression. Nowadays, machine learning has begun to apply to similar problems. In the 1950s and 1960s, there were attempts to combine the approaches to creating neural networks that existed at the time, which made it possible to calculate quantitative descriptions of human intelligence, and memorize, analyze, and process information, which resembled the work of the human brain. Medicine is one of the main areas of human activity where various classifier and neural network algorithms are gaining popularity yearly. They are trendy in disease diagnostics. Results: the initial conditions for choosing the best model are met by logistic regression. Conclusions: as a result of the study, the optimal model for predicting the development of the disease was selected.