Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm.

J Jeba Sonia1, Prassanna Jayachandran2, Abdul Quadir Md2

  • 1Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, College of Engineering and Technology, Kattankulathur, Chennai 603203, India.

Diagnostics (Basel, Switzerland)
|February 25, 2023
PubMed
Summary

Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

2.6K
Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Attention-augmented hybrid framework with evolutionary optimization for robust deepfake detection.

Scientific reportsยท2026
Same author

Enhancing Diagnostic Decision-Making: Ensemble Learning Techniques for Reliable Stress Level Classification.

Diagnostics (Basel, Switzerland)ยท2023
Same author

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection.

Biomedicinesยท2023
Same author

Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease.

Biomedicinesยท2023
Same author

Stick-Slip Vibration Suppression in Drill String Using Observer-Based LQG Controller.

Sensors (Basel, Switzerland)ยท2022
Same author

An approach to forecast impact of Covid-19 using supervised machine learning model.

Software: practice & experienceยท2021
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)ยท2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)ยท2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)ยท2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)ยท2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)ยท2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)ยท2026
See all related articles
This summary is machine-generated.

A new multi-layer neural network algorithm effectively classifies diabetes mellitus types 1, 2, and gestational. This automated system achieves high accuracy, aiding in improved diagnosis and healthcare strategies for diabetes.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Endocrinology

Background:

  • Rising global prevalence of diabetes mellitus, a chronic condition linked to high blood sugar.
  • Existing diagnostic methods require enhancement for improved patient care and treatment strategies.
  • Diabetes mellitus encompasses three main types: type 1, type 2, and gestational diabetes, each with distinct characteristics.

Purpose of the Study:

  • To introduce a novel automated information system for classifying the three types of diabetes mellitus.
  • To develop and evaluate a multi-layer neural network no-prop algorithm for accurate diabetes diagnosis.
  • To enhance healthcare by providing an efficient and reliable diabetes classification tool.

Main Methods:

  • Implementation of a multi-layer neural network no-prop algorithm with distinct training and testing phases.
Keywords:
diabetes classificationgestationalmachine learningmulti-layer neural network

More Related Videos

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Related Experiment Videos

Last Updated: Aug 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
  • Utilizing an attribute-selection process to identify relevant features for classification.
  • Training the neural network in a multi-layer fashion, comparing normal, type 1, type 2, and gestational diabetes.

    • Main Results:

    • The proposed multi-layer neural network model achieved high performance metrics.
    • Maximum sensitivity and specificity values of 0.97 and 0.95 were attained, respectively.
    • An overall accuracy score of 97% was recorded for the classification of diabetes mellitus types.

    Conclusions:

    • The developed multi-layer neural network system provides a workable and efficient approach for diabetes mellitus classification.
    • The system demonstrates superior performance compared to existing models, offering a promising tool for clinical application.
    • This automated classification system can significantly aid in improving diabetes diagnosis and management strategies.