Publication date: 2023

DOI: 10.1007/978-3-031-21435-6_66

This article discusses the use of machine learning methods to predict the degree of threat for onset of type 2 diabetes mellitus in patients aged 25 years. Type 2 diabetes mellitus is a disease that complicates the course of other concomitant diseases, and predicting the threat of its occurrence is an important element in forming the trajectory of diagnosis and treatment of the patient. A binary classification model based on the logistic regression method has been proposed and developed. The developed approach identifies the threat of diabetes mellitus onset using indicators such as age, sex, body mass index (BMI) and glycated hemoglobin (HbA1c). The paper describes how to build a model, and how to generate and prepare data for binary logistic regression. To implement the described approaches, the Python programming language, the Jupyter Lab development environment and scikit-learn, scipy, pandas, and numpy packages were used. Performance analysis showed accuracy of the proposed model as 0.98 on test data. The developed software can be used as a separate application and be built as a module into the clinical decision support system #COMESYSO1120.

Издатель: Springer Science and Business Media Deutschland GmbH

Тип: Article