The Applicability of Machine Learning in Prediabetes Prediction

Vîrgolici Oana (), Vîrgolici Horia-Marius () and Bologa Ana Ramona ()
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Vîrgolici Oana: Bucharest University of Economic Studies, Bucharest, Romania
Vîrgolici Horia-Marius: UMF„Carol Davila”, Bucharest, Romania
Bologa Ana Ramona: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2023, vol. 17, issue 1, 1757-1768

Abstract: In over 60% of patients with prediabetes, the evolution to diabetes can be stopped by changing lifestyle. The prevalence of diabetes mellitus was 11.6% and of prediabetes was 16.5% in the Romanian population aged 20-79 years. The deficiencies are not only at the health system level, but also in the application of prediabetes criteria existing in accredited guidelines. Although these guidelines imposed by the international diabetes federation are constantly updated, many doctors do not apply the recommended steps for diagnosis and treatment. We review, in the first part of the paper, several studies in predicting diabetes, which used different algorithms and techniques. In this second part of the paper, we propose a machine learning approach for prediabetes prediction, which uses kNN (k-Nearest Neighbors), DT (decision tree), SVM (Support Vectors Machines) and Logistic Regression (LR) algorithms. We used a dataset with 125 persons (men and women), with the following features: gender (S), serum glucose (G), serum triglycerides (TG), serum high-density lipoprotein cholesterol (HDL), waist circumference (WC) and systolic blood pressure (SBP). We used standardized medical criterion named Adult Treatment Panel III Guidelines (ATP III), which specifies that prediabetes diagnosis can be established if at least three of five parameters are outside the scale of their normal values. We obtained, for both algorithms, encouraging results in evaluating the models (in terms of confusion matrix, f1_score, accuracy_score).

Keywords: diabetes; prediabetes; machine learning; k-Nearest Neighbors; Decision Tree; Support Vectors Machines; Logistic Regression (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:17:y:2023:i:1:p:1757-1768:n:23

DOI: 10.2478/picbe-2023-0156

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