Praveen Talari^1,*, A. Suresh², M. G. Kavitha³

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1053-1067, 2023, DOI:10.32604/iasc.2023.027024 - 06 June 2022

Abstract As per World Health Organization report which was released in the year of 2019, Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world. Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it. Among the diabetics, it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.… More >

Xuelian Du^1,#, Hao Shi^2,#, Haiyan Liu¹, Linghua Zhou¹, Anqun Xie¹, Jufang Guo^1,*

Oncologie, Vol.24, No.4, pp. 769-787, 2022, DOI:10.32604/oncologie.2022.026248 - 31 December 2022

Abstract Background: Gestational diabetes mellitus (GDM) affects the health of numerous women around the world. A recent study has shown that GDM is associated with an increased incidence of cancer. In this study, we aimed to explore the possible shared mechanisms and potential common therapeutic targets between GDM and cancer. Methods: The limma package was used to identify differentially expressed genes (DEGs) in GDM. The Cytoscape plugin cytoHubba was used to screen hub genes. The CIBERSORT algorithm was used to explore the correlation between hub genes and immunity. Cox regression analysis was used to assess the relationship… More >

R. Rajkamal^1,*, Anitha Karthi², Xiao-Zhi Gao³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2013-2033, 2022, DOI:10.32604/cmc.2022.027142 - 18 May 2022

Abstract Diabetes is increasing commonly in people’s daily life and represents an extraordinary threat to human well-being. Machine Learning (ML) in the healthcare industry has recently made headlines. Several ML models are developed around different datasets for diabetic prediction. It is essential for ML models to predict diabetes accurately. Highly informative features of the dataset are vital to determine the capability factors of the model in the prediction of diabetes. Feature engineering (FE) is the way of taking forward in yielding highly informative features. Pima Indian Diabetes Dataset (PIDD) is used in this work, and the… More >

Elias Hossain¹, Mohammed Alshehri², Sultan Almakdi^2,*, Hanan Halawani², Md. Mizanur Rahman³, Wahidur Rahman⁴, Sabila Al Jannat⁵, Nadim Kaysar⁶, Shishir Mia⁴

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1713-1746, 2022, DOI:10.32604/cmc.2022.024822 - 24 February 2022

Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering… More >

Saleh Albahli^1,2,*, Ghulam Nabi Ahmad Hassan Yar³

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 837-853, 2022, DOI:10.32604/iasc.2022.024427 - 08 February 2022

Abstract Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of… More >

YANQIU LI^#,*, CHENJUN HAO^#, WEIYI CHEN, QINGJU MENG

BIOCELL, Vol.46, No.6, pp. 1565-1573, 2022, DOI:10.32604/biocell.2022.018347 - 07 February 2022

Abstract This work aimed to clarify the interaction between the fetus and pregnant patients with gestational diabetes mellitus (GDM), the lipid metabolomics analysis of the fetal umbilical cord blood of GDM patients and normal pregnant women were performed to screen out the specific lipid metabolites for pathogenesis of GDM. From 2019–2020, 21 patients with GDM and 22 normal pregnant women were enrolled in Hexian Memorial Hospital, Panyu District, Guangzhou. The general information such as weight, height, age, body mass index (BMI) before pregnancy were analyzed. Non-targeted metabonomic detection and analysis were performed in umbilical cord plasma… More >

Altyeb Altaher Taha^*, Sharaf Jameel Malebary

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6089-6105, 2022, DOI:10.32604/cmc.2022.023848 - 14 January 2022

Abstract Diabetes is a chronic health condition that impairs the body's ability to convert food to energy, recognized by persistently high levels of blood glucose. Undiagnosed diabetes can cause many complications, including retinopathy, nephropathy, neuropathy, and other vascular disorders. Machine learning methods can be very useful for disease identification, prediction, and treatment. This paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic regression. The proposed approach consists of two levels. First, a base-learner comprising six machine learning algorithms is utilized for predicting diabetes.… More >

T. Viveka^1,*, C. Christopher Columbus², N. Senthil Velmurugan³

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 535-547, 2022, DOI:10.32604/iasc.2022.022976 - 05 January 2022

Abstract Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of… More >

Fawaz Alassery¹, Ahmed Alzahrani², Asif Irshad Khan², Ashi Khan^3,*, Mohd Nadeem⁴, Md Tarique Jamal Ansari⁴

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1701-1715, 2022, DOI:10.32604/iasc.2022.023314 - 09 December 2021

Abstract A large number of people live in diabetes worldwide. Type-2 Diabetes (D2) accounts for 92% of patients with D2 and puts a huge burden on the healthcare industry. This multi-criterion medical research is based on the data collected from the hospitals of Uttar Pradesh, India. In recent times there is a need for a web-based electronic system to determine the impact of mental health in D2 patients. This study will examine the impact assessment in D2 patients. This paper used the integrated methodology of Fuzzy Analytic Hierarchy (FAHP) and Fuzzy Technique for Order Performance by… More >

SHADY G. EL-SAWAH^1,*, FAYEZ ALTHOBAITI², HANAN M. RASHWAN¹, ADIL ALDHAHRANI³, MARWA A. ABDEL-DAYEM⁴, EMAN FAYAD², REHAB M. AMEN⁵, EL SHAIMAA SHABANA⁶, EHAB I. EL-HALLOUS⁷

BIOCELL, Vol.46, No.3, pp. 745-757, 2022, DOI:10.32604/biocell.2022.017853 - 18 November 2021

Abstract Since Type 1 diabetes (T1DM) occurs when β-cells mass is reduced to less than 20% of the normal level due to autoimmune destruction of cells resulting in the inability to secrete insulin, preservation or replenishment of the functional β-cells mass has become a major therapeutic focus for this diabetic type treatment. Thus, this 4-week work plan was designed to determine which mesenchymal stem cells (MSCs) type is more appropriate to alleviate pancreatic hazards resulting from diabetes induction; via tracking a comparative study between MSCs derived from adipose tissue (AD-MSCs) and from bone marrow (BM-MSCs) in… More >