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  • Open Access

    ARTICLE

    Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

    Elias Hossain1, Mohammed Alshehri2, Sultan Almakdi2,*, 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 >

  • Open Access

    ARTICLE

    Detection of Diabetic Retinopathy Using Custom CNN to Segment the Lesions

    Saleh Albahli1,2,*, 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 >

  • Open Access

    ARTICLE

    Analysis of specific lipid metabolites in cord blood of patients with gestational diabetes mellitus

    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 >

  • Open Access

    ARTICLE

    A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction

    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 >

  • Open Access

    ARTICLE

    To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

    T. Viveka1,*, 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 >

  • Open Access

    ARTICLE

    Quantitative Evaluation of Mental-Health in Type-2 Diabetes Patients Through Computational Model

    Fawaz Alassery1, Ahmed Alzahrani2, Asif Irshad Khan2, Ashi Khan3,*, 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 >

  • Open Access

    ARTICLE

    Anti-inflammatory and antioxidant potential capacities of AD-MSCs and BM-MSCs in suppressing pancreatic β-cells auto-immunity and apoptosis in rats with T1DM induced model

    SHADY G. EL-SAWAH1,*, 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 >

  • Open Access

    ARTICLE

    Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

    Milos Mravik1, T. Vetriselvi2, K. Venkatachalam3,*, Marko Sarac1, Nebojsa Bacanin1, Sasa Adamovic1

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 457-471, 2022, DOI:10.32604/cmc.2022.020687 - 03 November 2021

    Abstract At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a… More >

  • Open Access

    ARTICLE

    Hybrid Online Model for Predicting Diabetes Mellitus

    C. Mallika1,*, S. Selvamuthukumaran2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1873-1885, 2022, DOI:10.32604/iasc.2022.020543 - 09 October 2021

    Abstract Modern healthcare systems have become smart by synergizing the potentials of wireless sensors, the medical Internet of things, and big data science to provide better patient care while decreasing medical expenses. Large healthcare organizations generate and accumulate an incredible volume of data continuously. The already daunting volume of medical information has a massive amount of diagnostic features and logged details of patients for certain diseases such as diabetes. Diabetes mellitus has emerged as along-haul fatal disease across the globe and particularly in developing countries. Exact and early diagnosis of diabetes from big medical data is… More >

  • Open Access

    ARTICLE

    Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

    Noha E. El-Attar1,*, Bossy M. Moustafa2, Wael A. Awad3

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 325-338, 2022, DOI:10.32604/iasc.2022.019970 - 03 September 2021

    Abstract DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein. Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken.… More >

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