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

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

574
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
574
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
34

You might also read

Related Articles

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

Sort by
Same author

On the usage of artificial intelligence in leprosy care: A systematic literature review.

PLoS computational biology·2025
Same author

Artificial Intelligence for Women and Child Healthcare: Is AI Able to Change the Beginning of a New Story? A Perspective.

Health science reports·2025
Same author

Correction: Tips from an expert panel on the development of a clinical research protocol.

BMC medical research methodology·2024
Same author

Tips from an expert panel on the development of a clinical research protocol.

BMC medical research methodology·2024
Same author

A comparative analysis of converters of tabular data into image for the classification of Arboviruses using Convolutional Neural Networks.

PloS one·2023
Same author

Predicting congenital syphilis cases: A performance evaluation of different machine learning models.

PloS one·2023

Related Experiment Video

Updated: May 17, 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

6.6K

Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning

Anna Beatriz Silva1, Elisson da Silva Rocha1, João Fausto Lorenzato1

  • 1Universidade de Pernambuco, Pernambuco, Brazil.

Plos One
|April 2, 2025
PubMed
Summary

This study improved premature birth prediction using machine learning and data balancing techniques. Hybrid sampling methods enhanced model accuracy, offering better support for maternal and neonatal care within Brazil's health system.

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.2K

Related Experiment Videos

Last Updated: May 17, 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

6.6K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.2K

Area of Science:

  • Medical Informatics
  • Machine Learning
  • Public Health

Background:

  • Premature birth (before 37 weeks gestation) is a leading cause of neonatal mortality globally.
  • Predictive modeling for preterm birth faces challenges due to imbalanced datasets, potentially leading to biased outcomes.
  • The Brazilian healthcare system (SUS) seeks improved tools for early identification of high-risk pregnancies.

Purpose of the Study:

  • To evaluate machine learning models for predicting premature birth using Brazilian data.
  • To address data imbalance issues using various sampling techniques.
  • To enhance the accuracy of preterm birth prediction for improved clinical intervention.

Main Methods:

  • Utilized a dataset of over 483,000 Brazilian sociodemographic and obstetric cases.
  • Compared five data balancing techniques: Undersampling, Oversampling, and three Hybrid-sampling configurations.
  • Trained and evaluated Decision Tree, Random Forest, and AdaBoost machine learning models.

Main Results:

  • Hybrid-sampling techniques significantly outperformed Undersampling and Oversampling in predictive model performance.
  • The Decision Tree model with Hybrid-sampling achieved 70% accuracy, 64% recall, and 74% precision.
  • Demonstrated the critical role of appropriate data balancing in developing reliable preterm birth prediction models.

Conclusions:

  • Hybrid-sampling is a superior approach for balancing imbalanced data in preterm birth prediction models.
  • Improved prediction accuracy can facilitate earlier identification of at-risk pregnancies, enabling timely interventions.
  • The findings have significant implications for enhancing maternal and neonatal care within the Brazilian Unified Health System (SUS), potentially reducing neonatal mortality.