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

Abstract

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.

Artificial Intelligence‐Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

“…Other studies [28][29][30] combined software and hardware, as did our study and were related to diabetes; however, they focused only on diabetic retinopathy, which is a complication of diabetes. Similar studies have attempted to use a convenient and noninvasive method to diagnose diabetes and have achieved an accuracy score higher than 0.85 [31,32]. Nevertheless, study [31] Table 2: Example of detected tongue conditions.…”

“…Similar studies have attempted to use a convenient and noninvasive method to diagnose diabetes and have achieved an accuracy score higher than 0.85 [31,32]. Nevertheless, study [31] Table 2: Example of detected tongue conditions. Three types of physical patterns that can be detected and analyzed are shown: both sides of the tongue and the fur of the tongue.…”

An Ontology Driven System to Predict Diabetes With Machine Learning Techniques

“…Currently blood sugar level and urine sugar level are measured to detect the diabetes. These parameters to detect diabetes have failed as symptoms of diabetic are ambiguous and the procedure involved in detecting is painful, concerning children [3]. There are numerous ways to predict diabetes in early stage in the growing technological world.…”

“…They are used in classification of medical data and use it for prediction. The idea of decision is based on if-then rule, using a tree structure beginning with a single node, then node getting expanded as a leaf [3,8]. Random forest is a combination of a greater number of Decision tree and it is a multi-functional algorithm, after training the samples unseen samples can be found from the trained samples [4].…”

Special Issue on Machine Learning for Biomedical Data Analysis

“…Czmil et al [7] proposed a method for a non-invasive method of type 1 diabetes detection. The proposed method is based on the physical activity measurement of patients.…”