Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 10, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Prediction of neural tube defect using support vector machine.

Jin-Feng Wang¹, Xin Liu, Yi-Lan Liao

¹State key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. wangjf@Lreis.ac.cn

Biomedical and Environmental Sciences : BES

|August 17, 2010

Summary

This study demonstrates that Support Vector Machine (SVM) can predict neural tube defects (NTDs). The machine learning model achieved moderate accuracy in identifying NTD risk at the village level.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

METTL3 loss exacerbates colitis via m<sup>6</sup>A-dependent Slc39a8 regulation and epithelial lineage depletion.

Acta pharmacologica Sinica·2026

Same author

Leveraging ensemble learning for predicting 30-day readmission in heart failure ICU patients.

Digital health·2026

Same author

[Simulation of Multi-scenario Land Use Change and Carbon Storage in Protected Areas of Beijing].

Huan jing ke xue= Huanjing kexue·2025

Same author

Evidence for cognitive compensation mechanism in the postoperative delirium: a prospective multi-modal neuroimaging cohort study.

Brain imaging and behavior·2025

Same author

Interferon-stimulated circHOMER1 attenuates antiviral innate immunity.

mBio·2025

Same author

A Novel Electrocatalytic Oxygen-Superoxide Pathway Driven by an Auxiliary Ligand-Engineered Nonheme Iron Catalyst for Highly Efficient Hydroxyl Radical Generation in Water Purification.

Environmental science & technology·2025

Area of Science:

Public Health
Biostatistics
Machine Learning

Background:

Neural tube defects (NTDs) represent a significant global health concern.
Accurate prediction of NTD prevalence is crucial for targeted interventions and resource allocation.

Purpose of the Study:

To evaluate the efficacy of Support Vector Machine (SVM) for predicting neural tube defects (NTDs).
To assess the applicability of machine learning in epidemiological surveillance of birth defects.

Main Methods:

A dataset from a pilot area was partitioned into training and testing sets.
Support Vector Machine (SVM) was trained on the designated training data.
The trained SVM model was employed to classify and predict NTD occurrences.

Related Experiment Videos

Last Updated: Jun 10, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Main Results:

The study successfully predicted NTD rates at the village level.
Prediction accuracy for the training dataset was 71.50%.
Prediction accuracy for the independent test dataset was 68.57%.

Conclusions:

Support Vector Machine (SVM) demonstrates applicability as a predictive tool for neural tube defects (NTDs).
Machine learning approaches show promise for enhancing NTD surveillance and prediction capabilities.