<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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).</p>
</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).

The Applicability of Machine Learning in Prediabetes Prediction

Proceedings of the International Conference on Business Excellence

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"The Applicability of Machine Learning in Prediabetes Prediction"}

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...

The Applicability of Machine Learning in Prediabetes Prediction

Publié en ligne: 14 juil. 2023

Pages: 1757 - 1768

DOI: https://doi.org/10.2478/picbe-2023-0156

Mots clés
diabetes, prediabetes, machine learning, k-Nearest Neighbors, Decision Tree, Support Vectors Machines, Logistic Regression

© 2023 Oana Vîrgolici et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Applicability of Machine Learning in Prediabetes Prediction

Publié en ligne: 14 juil. 2023

Pages: 1757 - 1768

DOI: https://doi.org/10.2478/picbe-2023-0156

Mots clésdiabetes, prediabetes, machine learning, k-Nearest Neighbors, Decision Tree, Support Vectors Machines, Logistic Regression

© 2023 Oana Vîrgolici et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
diabetes, prediabetes, machine learning, k-Nearest Neighbors, Decision Tree, Support Vectors Machines, Logistic Regression