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Open AccessArticle

A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device

Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2555;
Received: 28 May 2019 / Revised: 17 June 2019 / Accepted: 19 June 2019 / Published: 22 June 2019
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
Type 1 diabetes is a chronic disease marked by high blood glucose levels, called hyperglycemia. Diagnosis of diabetes typically requires one or more blood tests. The aim of this paper is to discuss a non-invasive method of type 1 diabetes detection, based on physical activity measurement. We solved a binary classification problem using a variety of computational intelligence methods, including non-linear classification algorithms, which were applied and comparatively assessed. Prediction of disease presence among children and adolescents was evaluated using performance measures, such as accuracy, sensitivity, specificity, precision, the goodness index, and AUC. The most satisfying results were obtained when using the random forest method. The primary parameters in disease detection were weekly step count and the weekly number of vigorous activity minutes. The dependance between the weekly number of steps and the type 1 diabetes presence was established after an insightful analysis of data using classification and clustering algorithms. The findings have shown promising results that type 1 diabetes can be diagnosed using physical activity measurement. This is essential regarding the non-invasiveness and flexibility of the detection method, which can be tested at any time anywhere. The proposed technique can be implemented on a mobile device. View Full-Text
Keywords: type 1 diabetes; classification; physical activity; artificial intelligence type 1 diabetes; classification; physical activity; artificial intelligence
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MDPI and ACS Style

Czmil, A.; Czmil, S.; Mazur, D. A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device. Appl. Sci. 2019, 9, 2555.

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