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 Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

2.1K
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...
2.1K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

102
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
102
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

499
For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
499
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.3K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
12.3K
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

2.4K
Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
2.4K

You might also read

Related Articles

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

Sort by
Same author

Designing Scalable Mechano-Virucidal Nanostructured Acrylic Surfaces for Enhanced Viral Inactivation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Developing count regression techniques for predicting the number of new type 2 diabetes cases in Saudi Arabia.

PloS one·2026
Same author

Predicting early and late neonatal mortality using machine learning models in Oman.

BMC public health·2025
Same author

Enhancing node influence prediction in large networks via multi-Level knowledge distillation.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Antimicrobial activity of some bacteria isolated from marine sponge in Neom region, Saudi Arabia.

BMC microbiology·2025
Same author

Comparative outcomes of culprit-only versus complete revascularisation in cardiogenic shock complicating acute myocardial infarction: insights from the Gulf-Cardiogenic Shock registry.

BMJ open·2025
Same journal

Thymidylate synthase inhibitory drugs induce p53-dependent pathways differently.

PloS one·2026
Same journal

Top-down and bottom-up attention for joint pattern classification and reconstruction.

PloS one·2026
Same journal

Short- and long-term scaling behavior of blood pressure and pulse arrival time during sleep in healthy controls and patients with obstructive sleep apnea.

PloS one·2026
Same journal

Double DQN-based secrecy energy efficiency and fairness performance in IRS-assisted NOMA systems with friendly jamming.

PloS one·2026
Same journal

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century.

PloS one·2026
Same journal

Paying in public: Peer effects, impression management, and willingness to pay on digital payment platforms.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 28, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

Predicting Type 2 diabetes onset age using machine learning: A case study in KSA.

Faten Al-Hussein1,2, Laleh Tafakori1, Mali Abdollahian1

  • 1School of Science, RMIT University, Melbourne, Victoria, Australia.

Plos One
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

Predicting Type 2 Diabetes (T2D) onset age in Saudi Arabia is crucial for early intervention. Machine learning models identified key factors like lipid profiles and BMI, aiding in proactive healthcare strategies for this prevalent disease.

More Related Videos

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

2.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

Related Experiment Videos

Last Updated: May 28, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

2.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

Area of Science:

  • Endocrinology and Metabolism
  • Medical Informatics
  • Public Health

Background:

  • Type 2 Diabetes (T2D) prevalence is rising in Saudi Arabia, posing significant healthcare challenges.
  • Early prediction of T2D age at onset is vital for timely interventions and complication reduction.
  • Saudi Arabia ranks 7th globally in T2D prevalence, highlighting the need for regional studies.

Purpose of the Study:

  • To predict the age at onset of Type 2 Diabetes (T2D) in Saudi Arabia.
  • To identify key predictors influencing T2D onset age using various machine learning models.
  • To provide tools for healthcare practitioners for monitoring and intervention strategies.

Main Methods:

  • Utilized Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR).
  • Developed models using medical records of 1,000 diabetic patients (2018-2022) including demographic, lifestyle, and lipid profile data.
  • Employed logarithmic transformation of onset age for model development.

Main Results:

  • The average T2D onset age was 65 years, with the most common range between 40 and 90 years.
  • MLR and RF models demonstrated the best performance, with R2 values of 0.90 and 0.89, respectively.
  • Key predictors identified include triglycerides, total cholesterol, HDL, ferritin, BMI, SBP, WBC, diet, and vitamin D levels.

Conclusions:

  • This study is the first in Saudi Arabia to apply MLR, ANN, RF, SVR, and DTR for predicting T2D onset age.
  • The developed models offer valuable tools for predicting T2D onset and identifying at-risk individuals.
  • Findings can inform targeted intervention strategies to mitigate the impact of T2D in Saudi Arabia.