type 2 diabetes mellitus
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2022 ◽  
Vol 28 ◽  
pp. 82-89
Budi Hidayat ◽  
Royasia Viki Ramadani ◽  
Achmad Rudijanto ◽  
Pradana Soewondo ◽  
Ketut Suastika ◽  

Ratna Patil ◽  
Sharvari Tamane ◽  
Shitalkumar Adhar Rawandale ◽  
Kanishk Patil

<p>Diabetes mellitus is a chronic disease that affects many people in the world badly. Early diagnosis of this disease is of paramount importance as physicians and patients can work towards prevention and mitigation of future complications. Hence, there is a necessity to develop a system that diagnoses type 2 diabetes mellitus (T2DM) at an early stage. Recently, large number of studies have emerged with prediction models to diagnose T2DM. Most importantly, published literature lacks the availability of multi-class studies. Therefore, the primary objective of the study is development of multi-class predictive model by taking advantage of routinely available clinical data in diagnosing T2DM using machine learning algorithms. In this work, modified mayfly-support vector machine is implemented to notice the prediabetic stage accurately. To assess the effectiveness of proposed model, a comparative study was undertaken and was contrasted with T2DM prediction models developed by other researchers from last five years. Proposed model was validated over data collected from local hospitals and the benchmark PIMA dataset available on UCI repository. The study reveals that modified Mayfly-SVM has a considerable edge over metaheuristic optimization algorithms in local as well as global searching capabilities and has attained maximum test accuracy of 94.5% over PIMA.</p>

İsmail Dündar ◽  
Ayşehan Akıncı

Abstract Objectives The aim of the study was to determine the prevalence of metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), and other comorbidities in overweight and obese children in Malatya, Turkey. Methods Retrospective cross-sectional study. We studied 860 obese and overweight children and adolescents (obese children Body mass index (BMI) >95th percentile, overweight children BMI >85th percentile) aged between 6 and 18 years. The diagnosis of MetS, impaired glucose tolerance (IGT), impaired fasting glucose (IFG), and T2DM were defined according to modified the World Health Organization criteria adapted for children. Other comorbidities were studied. Results Subjects (n=860) consisted of 113 overweight and 747 obese children of whom 434 (50.5%) were girls. MetS was significantly more prevalent in obese than overweight children (43.8 vs. 2.7%, p<0.001), and in pubertal than prepubertal children (41.1 vs. 31.7%, p<0.001). Mean homeostasis model assessment for insulin ratio (HOMA-IR) was 3.6 ± 2.0 in the prepubertal and 4.9 ± 2.4 in pubertal children (p<0.001). All cases underwent oral glucose tolerance test and IGT, IFG, and T2DM were diagnosed in 124 (14.4%), 19 (2.2%), and 32 (3.7%) cases, respectively. Insulin resistance (IR) was present in 606 cases (70.5%). Conclusions Puberty and obesity are important risk factors for MetS, T2DM, and IR. The prevalence of MetS, T2DM, and other morbidities was high in the study cohort. Obese children and adolescents should be carefully screened for T2DM, insulin resistance, hyperinsulinism, dyslipidemia, hypertension, IGT, and IFG. The prevention, early recognition, and treatment of obesity are essential to avoid associated morbidities.

Bárbara Aranha Ribeiro ◽  
Camilla Pedrosa Vieira Lima ◽  
Luana Severo Alves ◽  
Nailê Damé-Teixeira

2022 ◽  
Vol 13 (1) ◽  
pp. 5-26
Sarantis Livadas ◽  
Panagiotis Anagnostis ◽  
Julia K Bosdou ◽  
Dimitra Bantouna ◽  
Rodis Paparodis

2022 ◽  
Emilia Rymkiewicz ◽  
Grzegorz Dzida ◽  
Wojciech Myśliński ◽  
Andrzej Prystupa ◽  
Marcin Trojnar ◽  

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