Jove
Visualize
Contact Us

Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

417
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
417
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

249
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
249
Prediction Intervals01:03

Prediction Intervals

2.9K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.9K

You might also read

Related Articles

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

Sort by
Same author

Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review.

Health science reports·2026
Same author

Adaptation of global One Health evaluation framework to municipal levels in Fukuoka, Japan.

Infectious diseases of poverty·2025
Same author

Users' Perceived Service Quality of National Telemedicine Services During the COVID-19 Pandemic in Bangladesh: Cross-Sectional Study.

JMIR human factors·2025
Same author

The impact of limited access to digital health records on doctors and their willingness to adopt electronic health record systems.

Digital health·2024
Same author

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

JMIR diabetes·2023
Same author

Telehealth Care for Mothers and Infants to Improve the Continuum of Care: Protocol for a Quasi-Experimental Study.

JMIR research protocols·2022
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: Dec 6, 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

7.2K

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison.

Masuda Begum Sampa1, Md Nazmul Hossain2, Md Rakibul Hoque3

  • 1Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.

JMIR Medical Informatics
|October 8, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict blood uric acid levels in urban Bangladeshi corporate employees. Boosted decision tree regression achieved the best accuracy, aiding early detection of high uric acid to reduce health costs.

Keywords:
Bangladeshblood uric acidboosted decision tree regression modelmachine learningnoncommunicable diseasesurban corporate population

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.2K

Related Experiment Videos

Last Updated: Dec 6, 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

7.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.2K

Area of Science:

  • Biomedical informatics
  • Machine learning in healthcare
  • Noncommunicable disease risk assessment

Background:

  • Elevated uric acid is a risk factor for numerous noncommunicable diseases.
  • Research on uric acid prediction using machine learning is limited in developing countries, particularly for urban corporate populations in Bangladesh.
  • Noncommunicable diseases pose a significant health risk to this demographic.

Purpose of the Study:

  • To develop a predictive model for blood uric acid levels.
  • Utilize machine learning algorithms for prediction based on health checkup data, diet, and sociodemographics.
  • Reduce health management costs through accurate prediction of health checkup measurements.

Main Methods:

  • Employed various machine learning algorithms including boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression.
  • Addressed complex interactions within clinical input data, which conventional statistical models struggle with.
  • Evaluated model performance using data from 271 employees at Grameen Bank in Dhaka, Bangladesh.

Main Results:

  • The average uric acid level was 6.63 mg/dL, with many individuals showing borderline results.
  • Boosted decision tree regression demonstrated superior performance with a root mean squared error of 0.03.
  • The achieved accuracy surpasses previously reported models for uric acid prediction.

Conclusions:

  • A novel uric acid prediction model was successfully developed using personal characteristics, dietary information, and health checkup data.
  • The model can enhance awareness among high-risk individuals and populations, potentially lowering medical expenses.
  • Future research should incorporate additional factors like work stress, physical activity, and dietary habits to further refine prediction accuracy.