scholarly journals Mathematical Models and Machine Learning Algorithms in the Diagnosis of Complications of Type 1 Diabetes Mellitus

2021 ◽  
pp. 97-101
Author(s):  
O.S. Krotova ◽  
L.A. Khvorova ◽  
A.I. Piyanzin
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Children And Adolescents ◽  
Diabetic Polyneuropathy ◽  
Medical Data ◽  
Classification Model ◽  
Complications Of Diabetes

The paper deals with the problem of diabetic polyneuropathy diagnosing. This is one of the earliest and most dangerous complications of diabetes among children and adolescents. The research aims to develop models for diagnosing diabetic polyneuropathy in children and adolescents based on various medical data. The developed models will make it possible to diagnose a complication without using neurophysiological research methods. Therefore, the proposed models can be used in small medical and obstetrical stations in rural areas as well as a support system for making medical decisions. In the course of the study, a review and analysis of scientific publications of domestic and foreign scientists on the topic of the research are carried out. A large set of textual medical data is processed, then a database is created, features are analyzed, and a model is developed to reveal the presence of diabetic polyneuropathy in children and adolescents with type 1 diabetes mellitus. The achieved quality of the classification model allows us to assert that machine learning methods can be used to find hidden dependencies in the development and course of complications of diabetes mellitus.

Bone status of Indian children and adolescents with type 1 diabetes mellitus

2013 ◽  
Author(s):  
Parthasarathy Lavanya ◽  
Khadilkar Anuradha ◽  
Ekbote Veena ◽  
Chiplonkar Shashi ◽  
Mughal Zulf ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Children And Adolescents ◽  
Indian Children ◽  
Bone Status

795-P: Effect of Protein Intake (PI) on Postprandial Glycemia in Children and Adolescents with Type 1 Diabetes Mellitus

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 795-P
Author(s):  
DALIA DALLE ◽  
SARINE G. SHAHMIRIAN ◽  
MARYANN O'RIORDAN ◽  
TERESA N. ZIMMERMAN ◽  
JAMIE R. WOOD
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Children And Adolescents ◽  
Protein Intake ◽  
Postprandial Glycemia

scholarly journals A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Support Vector ◽  
Chronic Hyperglycemia ◽  
Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

Prevalence of autoantibodies in type 1 diabetes mellitus pediatrics in Mazandaran, North of Iran

2020 ◽  
Vol 33 (10) ◽  
pp. 1299-1305
Author(s):  
Daniel Zamanfar ◽  
Mohsen Aarabi ◽  
Monireh Amini ◽  
Mahila Monajati
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Serum Level ◽  
Type 1 Diabetes Mellitus ◽  
Children And Adolescents ◽  
Statistical Significance ◽  
Pancreatic Beta Cell ◽  
Eligible Patient ◽  
Diabetes Diagnosis

AbstractObjectivesType 1 diabetes is an autoimmune disease. Its most important immunologic markers are pancreatic beta-cell autoantibodies. This study aimed to determine diabetes mellitus antibodies frequency among children and adolescents with type 1 diabetes.MethodsThis descriptive study evaluated the frequency of four diabetes autoantibodies (glutamic acid decarboxylase 65 autoantibodies [GADA], islet cell autoantibodies [ICA], insulin autoantibodies [IAA], tyrosine phosphatase–like insulinoma antigen-2 antibodies [IA-2A]) and their serum level in children and adolescents diagnosed with type 1 diabetes mellitus at the diabetes department of Bou-Ali-Sina Hospital and Baghban Clinic, Sari, Iran, from March 2012 to March 2018. The relationship between the level of different antibodies and age, gender, and diabetes duration were determined. A two-sided p value less than 0.05 indicated statistical significance.ResultsOne hundred forty-two eligible patient records were screened. The average age at diabetes diagnosis was 4.2 ± 4.4 years. The median duration of diabetes was 34.0 (12.7–69.7) months. 53.5% of patients were female, and 81.7% of them had at least one positive autoantibody, and ICA in 66.2%, GADA in 56.3%, IA-2A in 40.1%, and IAA in 21.8% were positive. The type of the autoantibodies and their serum level was similar between females and males but there was a higher rate of positive autoantibodies in females. The level of IA-2A and ICA were in positive and weak correlation with age at diagnosis.ConclusionsMore than 80% of pediatric and adolescent patients with type 1 diabetes were autoantibody-positive. ICA and GADA were the most frequently detected autoantibodies. The presence of antibodies was significantly higher in females.

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